Journal articles on the topic 'Ensembles'
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Sabzevari, Maryam, Gonzalo Martínez-Muñoz, and Alberto Suárez. "Building heterogeneous ensembles by pooling homogeneous ensembles." International Journal of Machine Learning and Cybernetics 13, no. 2 (October 13, 2021): 551–58. http://dx.doi.org/10.1007/s13042-021-01442-1.
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AbstractHeterogeneous ensembles consist of predictors of different types, which are likely to have different biases. If these biases are complementary, the combination of their decisions is beneficial and could be superior to homogeneous ensembles. In this paper, a family of heterogeneous ensembles is built by pooling classifiers from M homogeneous ensembles of different types of size T. Depending on the fraction of base classifiers of each type, a particular heterogeneous combination in this family is represented by a point in a regular simplex in M dimensions. The M vertices of this simplex represent the different homogeneous ensembles. A displacement away from one of these vertices effects a smooth transformation of the corresponding homogeneous ensemble into a heterogeneous one. The optimal composition of such heterogeneous ensemble can be determined using cross-validation or, if bootstrap samples are used to build the individual classifiers, out-of-bag data. The proposed heterogeneous ensemble building strategy, composed of neural networks, SVMs, and random trees (i.e. from a standard random forest), is analyzed in a comprehensive empirical analysis and compared to a benchmark of other heterogeneous and homogeneous ensembles. The achieved results illustrate the gains that can be achieved by the proposed ensemble creation method with respect to both homogeneous ensembles and to the tested heterogeneous building strategy at a fraction of the training cost.
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Kieburg, Mario. "Additive matrix convolutions of Pólya ensembles and polynomial ensembles." Random Matrices: Theory and Applications 09, no. 04 (November 8, 2019): 2150002. http://dx.doi.org/10.1142/s2010326321500027.
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Recently, subclasses of polynomial ensembles for additive and multiplicative matrix convolutions were identified which were called Pólya ensembles (or polynomial ensembles of derivative type). Those ensembles are closed under the respective convolutions and, thus, build a semi-group when adding by hand a unit element. They even have a semi-group action on the polynomial ensembles. Moreover, in several works transformations of the bi-orthogonal functions and kernels of a given polynomial ensemble were derived when performing an additive or multiplicative matrix convolution with particular Pólya ensembles. For the multiplicative matrix convolution on the complex square matrices the transformations were even done for general Pólya ensembles. In the present work, we generalize these results to the additive convolution on Hermitian matrices, on Hermitian anti-symmetric matrices, on Hermitian anti-self-dual matrices and on rectangular complex matrices. For this purpose, we derive the bi-orthogonal functions and the corresponding kernel for a general Pólya ensemble which was not done before. With the help of these results, we find transformation formulas for the convolution with a fixed matrix or a random matrix drawn from a general polynomial ensemble. As an example, we consider Pólya ensembles with an associated weight which is a Pólya frequency function of infinite order. But we also explicitly evaluate the Gaussian unitary ensemble as well as the complex Laguerre (aka Wishart, Ginibre or chiral Gaussian unitary) ensemble. All results hold for finite matrix dimension. Furthermore, we derive a recursive relation between Toeplitz determinants which appears as a by-product of our results.
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Zubarev, V. Yu, B. V. Ponomarenko, E. G. Shanin, and A. G. Vostretsov. "Formation of Minimax Ensembles of Aperiodic Gold Codes." Journal of the Russian Universities. Radioelectronics 23, no. 2 (April 28, 2020): 26–37. http://dx.doi.org/10.32603/1993-8985-2020-23-2-26-37.
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Introduction. Signals constructed on the basis of ensembles of code sequences are widely used in digital communication systems. During development of such systems, the most attention is paid to analysis, synthesis and implementation of periodic signal ensembles. Theoretic methods for synthesis of periodic signal ensembles are developed and are in use. Considerably fewer results are received regarding construction of aperiodic signal ensembles with given properties. Theoretical methods for synthesis of such ensembles are practically nonexistent.Aim. To construct aperiodic Gold code ensembles with the best ratios of code length to ensemble volume among the most known binary codes.Materials and methods. Methods of directed search and discrete choice of the best ensemble based on unconditional preference criteria are used.Results. Full and truncated aperiodic Gold code ensembles with given length and ensemble volume were constructed. Parameters and shape of auto- and mutual correlation functions were shown for a number of constructed ensembles. Comparison of the paper results with known results for periodic Gold code ensembles has been conducted regarding growth of minimax correlation function values depending on code length and ensemble volume.Conclusion. The developed algorithms, unlike the known ones, make it possible to form both complete ensembles and ensembles taking into account the limitation of their volume. In addition, the algorithms can be extended to the tasks of forming ensembles from other families, for example, assembled from code sequences belonging to different families.
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Alazba, Amal, and Hamoud Aljamaan. "Software Defect Prediction Using Stacking Generalization of Optimized Tree-Based Ensembles." Applied Sciences 12, no. 9 (April 30, 2022): 4577. http://dx.doi.org/10.3390/app12094577.
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Software defect prediction refers to the automatic identification of defective parts of software through machine learning techniques. Ensemble learning has exhibited excellent prediction outcomes in comparison with individual classifiers. However, most of the previous work utilized ensemble models in the context of software defect prediction with the default hyperparameter values, which are considered suboptimal. In this paper, we investigate the applicability of a stacking ensemble built with fine-tuned tree-based ensembles for defect prediction. We used grid search to optimize the hyperparameters of seven tree-based ensembles: random forest, extra trees, AdaBoost, gradient boosting, histogram-based gradient boosting, XGBoost and CatBoost. Then, a stacking ensemble was built utilizing the fine-tuned tree-based ensembles. The ensembles were evaluated using 21 publicly available defect datasets. Empirical results showed large impacts of hyperparameter optimization on extra trees and random forest ensembles. Moreover, our results demonstrated the superiority of the stacking ensemble over all fine-tuned tree-based ensembles.
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Kolczynski, Walter C., David R. Stauffer, Sue Ellen Haupt, Naomi S. Altman, and Aijun Deng. "Investigation of Ensemble Variance as a Measure of True Forecast Variance." Monthly Weather Review 139, no. 12 (December 1, 2011): 3954–63. http://dx.doi.org/10.1175/mwr-d-10-05081.1.
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Abstract The uncertainty in meteorological predictions is of interest for applications ranging from economic to recreational to public safety. One common method to estimate uncertainty is by using meteorological ensembles. These ensembles provide an easily quantifiable measure of the uncertainty in the forecast in the form of the ensemble variance. However, ensemble variance may not accurately reflect the actual uncertainty, so any measure of uncertainty derived from the ensemble should be calibrated to provide a more reliable estimate of the actual uncertainty in the forecast. A previous study introduced the linear variance calibration (LVC) as a simple method to determine the ensemble variance to error variance relationship and demonstrated this technique on real ensemble data. The LVC parameters, the slopes, and y intercepts, however, are generally different from the ideal values. This current study uses a stochastic model to examine the LVC in a controlled setting. The stochastic model is capable of simulating underdispersive and overdispersive ensembles as well as perfectly reliable ensembles. Because the underlying relationship is specified, LVC results can be compared to theoretical values of the slope and y intercept. Results indicate that all types of ensembles produce calibration slopes that are smaller than their theoretical values for ensemble sizes less than several hundred members, with corresponding y intercepts greater than their theoretical values. This indicates that all ensembles, even otherwise perfect ensembles, should be calibrated if the ensemble size is less than several hundred. In addition, it is shown that an adjustment factor can be computed for inadequate ensemble size. This adjustment factor is independent of the stochastic model and is applicable to any linear regression of error variance on ensemble variance. When applied to experiments using the stochastic model, the adjustment produces LVC parameters near their theoretical values for all ensemble sizes. Although the adjustment is unnecessary when applying LVC, it allows for a more accurate assessment of the reliability of ensembles, and a fair comparison of the reliability for differently sized ensembles.
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Van Peski, Roger. "Spectral distributions of periodic random matrix ensembles." Random Matrices: Theory and Applications 10, no. 01 (December 19, 2019): 2150011. http://dx.doi.org/10.1142/s2010326321500118.
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Koloğlu, Kopp and Miller compute the limiting spectral distribution of a certain class of real random matrix ensembles, known as [Formula: see text]-block circulant ensembles, and discover that it is exactly equal to the eigenvalue distribution of an [Formula: see text] Gaussian unitary ensemble. We give a simpler proof that under very general conditions which subsume the cases studied by Koloğlu–Kopp–Miller, real-symmetric ensembles with periodic diagonals always have limiting spectral distribution equal to the eigenvalue distribution of a finite Hermitian ensemble with Gaussian entries which is a ‘complex version’ of a [Formula: see text] submatrix of the ensemble. We also prove an essentially algebraic relation between certain periodic finite Hermitian ensembles with Gaussian entries, and the previous result may be seen as an asymptotic version of this for real-symmetric ensembles. The proofs show that this general correspondence between periodic random matrix ensembles and finite complex Hermitian ensembles is elementary and combinatorial in nature.
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Opitz, D., and R. Maclin. "Popular Ensemble Methods: An Empirical Study." Journal of Artificial Intelligence Research 11 (August 1, 1999): 169–98. http://dx.doi.org/10.1613/jair.614.
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An ensemble consists of a set of individually trained classifiers (such as neural networks or decision trees) whose predictions are combined when classifying novel instances. Previous research has shown that an ensemble is often more accurate than any of the single classifiers in the ensemble. Bagging (Breiman, 1996c) and Boosting (Freund & Shapire, 1996; Shapire, 1990) are two relatively new but popular methods for producing ensembles. In this paper we evaluate these methods on 23 data sets using both neural networks and decision trees as our classification algorithm. Our results clearly indicate a number of conclusions. First, while Bagging is almost always more accurate than a single classifier, it is sometimes much less accurate than Boosting. On the other hand, Boosting can create ensembles that are less accurate than a single classifier -- especially when using neural networks. Analysis indicates that the performance of the Boosting methods is dependent on the characteristics of the data set being examined. In fact, further results show that Boosting ensembles may overfit noisy data sets, thus decreasing its performance. Finally, consistent with previous studies, our work suggests that most of the gain in an ensemble's performance comes in the first few classifiers combined; however, relatively large gains can be seen up to 25 classifiers when Boosting decision trees.
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Du, Juan, Fei Zheng, He Zhang, and Jiang Zhu. "A Multivariate Balanced Initial Ensemble Generation Approach for an Atmospheric General Circulation Model." Water 13, no. 2 (January 7, 2021): 122. http://dx.doi.org/10.3390/w13020122.
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Based on the multivariate empirical orthogonal function (MEOF) method, a multivariate balanced initial ensemble generation method was applied to the ensemble data assimilation scheme. The initial ensembles were generated with a reasonable consideration of the physical relationships between different model variables. The spatial distribution derived from the MEOF analysis is combined with the 3-D random perturbation to generate a balanced initial perturbation field. The Local Ensemble Transform Kalman Filter (LETKF) data assimilation scheme was established for an atmospheric general circulation model. Ensemble data assimilation experiments using different initial ensemble generation methods, spatially random and MEOF-based balanced, are performed using realistic atmospheric observations. It is shown that the ensembles integrated from the balanced initial ensembles maintain a much more reasonable spread and a more reliable horizontal correlation compared with the historical model results than those from the randomly perturbed initial ensembles. The model predictions were also improved by adopting the MEOF-based balanced initial ensembles.
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Du, Juan, Fei Zheng, He Zhang, and Jiang Zhu. "A Multivariate Balanced Initial Ensemble Generation Approach for an Atmospheric General Circulation Model." Water 13, no. 2 (January 7, 2021): 122. http://dx.doi.org/10.3390/w13020122.
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Based on the multivariate empirical orthogonal function (MEOF) method, a multivariate balanced initial ensemble generation method was applied to the ensemble data assimilation scheme. The initial ensembles were generated with a reasonable consideration of the physical relationships between different model variables. The spatial distribution derived from the MEOF analysis is combined with the 3-D random perturbation to generate a balanced initial perturbation field. The Local Ensemble Transform Kalman Filter (LETKF) data assimilation scheme was established for an atmospheric general circulation model. Ensemble data assimilation experiments using different initial ensemble generation methods, spatially random and MEOF-based balanced, are performed using realistic atmospheric observations. It is shown that the ensembles integrated from the balanced initial ensembles maintain a much more reasonable spread and a more reliable horizontal correlation compared with the historical model results than those from the randomly perturbed initial ensembles. The model predictions were also improved by adopting the MEOF-based balanced initial ensembles.
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Hartono, Hartono, Opim Salim Sitompul, Tulus Tulus, Erna Budhiarti Nababan, and Darmawan Napitupulu. "Hybrid Approach Redefinition (HAR) model for optimizing hybrid ensembles in handling class imbalance: a review and research framework." MATEC Web of Conferences 197 (2018): 03003. http://dx.doi.org/10.1051/matecconf/201819703003.
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The purpose of this research is to develop a research framework to optimize the results of hybrid ensembles in handling class imbalance issues. The imbalance class is a state in which the classification results give the number of instances in a class much larger than the number of instances in the other class. In machine learning, this problem can reduce the prediction accuracy and also reduce the quality of the resulting decisions. One of the most popular methods of dealing with class imbalance is the method of ensemble learning. Hybrid Ensembles is an ensemble learning method approach that combines the use of bagging and boosting. Optimization of Hybrid Ensembles is done with the intent to reduce the number of classifier and also obtain better data diversity. Based on an iterative methodology, we review, analyze, and synthesize the current state of the literature and propose a completely new research framework for optimizing Hybrid Ensembles. In doing so, we propose a new taxonomy in ensemble learning that yields a new approach of sampling-based Ensembles and will propose an optimization Hybrid Ensembles using Hybrid Approach Redefinition (HAR) Model that combines the use of Hybrid Ensembles and Sampling Based Ensembles methods. We further provide an empirical analysis of the reviewed literature and emphasize the benefits that can be achieved by optimizing Hybrid Ensembles.
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KO, ALBERT HUNG-REN, ROBERT SABOURIN, and ALCEU DE SOUZA BRITTO. "COMPOUND DIVERSITY FUNCTIONS FOR ENSEMBLE SELECTION." International Journal of Pattern Recognition and Artificial Intelligence 23, no. 04 (June 2009): 659–86. http://dx.doi.org/10.1142/s021800140900734x.
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An effective way to improve a classification method's performance is to create ensembles of classifiers. Two elements are believed to be important in constructing an ensemble: (a) the performance of each individual classifier; and (b) diversity among the classifiers. Nevertheless, most works based on diversity suggest that there exists only weak correlation between classifier performance and ensemble accuracy. We propose compound diversity functions which combine the diversities with the performance of each individual classifier, and show that there is a strong correlation between the proposed functions and ensemble accuracy. Calculation of the correlations with different ensemble creation methods, different problems and different classification algorithms on 0.624 million ensembles suggests that most compound diversity functions are better than traditional diversity measures. The population-based Genetic Algorithm was used to search for the best ensembles on a handwritten numerals recognition problem and to evaluate 42.24 million ensembles. The statistical results indicate that compound diversity functions perform better than traditional diversity measures, and are helpful in selecting the best ensembles.
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Sanderson, Benjamin M. "A Multimodel Study of Parametric Uncertainty in Predictions of Climate Response to Rising Greenhouse Gas Concentrations." Journal of Climate 24, no. 5 (March 1, 2011): 1362–77. http://dx.doi.org/10.1175/2010jcli3498.1.
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Abstract One tool for studying uncertainties in simulations of future climate is to consider ensembles of general circulation models where parameterizations have been sampled within their physical range of plausibility. This study is about simulations from two such ensembles: a subset of the climateprediction.net ensemble using the Met Office Hadley Centre Atmosphere Model, version 3.0 and the new “CAMcube” ensemble using the Community Atmosphere Model, version 3.5. The study determines that the distribution of climate sensitivity in the two ensembles is very different: the climateprediction.net ensemble subset range is 1.7–9.9 K, while the CAMcube ensemble range is 2.2–3.2 K. On a regional level, however, both ensembles show a similarly diverse range in their mean climatology. Model radiative flux changes suggest that the major difference between the ranges of climate sensitivity in the two ensembles lies in their clear-sky longwave responses. Large clear-sky feedbacks present only in the climateprediction.net ensemble are found to be proportional to significant biases in upper-tropospheric water vapor concentrations, which are not observed in the CAMcube ensemble. Both ensembles have a similar range of shortwave cloud feedback, making it unlikely that they are causing the larger climate sensitivities in climateprediction.net. In both cases, increased negative shortwave cloud feedbacks at high latitudes are generally compensated by increased positive feedbacks at lower latitudes.
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Fiskvik, Anne Margrete. "La Famille Dansant. Investigating the Family Structure and Repertory of the Johannesénske Balletselskab." Nordic Theatre Studies 27, no. 2 (August 30, 2015): 104. http://dx.doi.org/10.7146/nts.v27i2.24254.
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The performance history of the Johannesénske Balletselskab spans a long period. In different shapes, sizes and names the ensemble was on the road for 30 years. This article analyses the activities of the Johannesénske enterprise through the lenses of itinerant performance traditions. Two features are discussed in this article: the reliance on family members as performers and the ensemble’s diverse repertory. The ensemble featured a repertory popular in its own time, consisting chiefly of national and character dances as well as pantomimes. Arguably, an investigation of the ensemble’s performance history offers information on little explored perspectives of nineteenth century Nordic ballet. In particular, the Johannesénske Balletselskab offers insights into family structures and the repertoire typical of itinerant ensembles. This information is also useful on a more general level, given that there were several ensembles similar to the Johannesénske travelling in the Nordic countries that functioned similarly to La famille dansant. Through analysing the use of the repertoire, it can be shown that the Johannesénske enterprise was, in their programming, up to date as a “ballet ensemble”. The family kept up itinerant traditions through their use of children as performers and reliance on pantomimes. The Johannesénske ensemble is, therefore, especially valuable for revealing insights into dance practices and dance styles, which have, so far, been little researched.
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Yuan, Wendao, Zhaoqi Wu, and Shao-Ming Fei. "Characterizing the quantumness of mixed-state ensembles via the coherence of Gram matrix with generalized α-z-relative Rényi entropy." Laser Physics Letters 19, no. 12 (October 25, 2022): 125203. http://dx.doi.org/10.1088/1612-202x/ac9970.
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Abstract The Gram matrix of an ensemble of pure states can be regarded as a quantum state, and the quantumness of the ensemble can be quantified by the coherence of the Gram matrix. By using the affinity between mixed states, the concept of Gram matrix of pure-state ensembles can be extended to the one of mixed-state ensembles. By utilizing the generalized α-z-relative Rényi entropy of coherence of Gram matrices, we present a new quantifier of quantumness of mixed-sate ensembles and further reveal its peculiar properties. To illustrate our quantumness of mixed-sate ensembles, we also calculate the quantumness for some detailed mixed-sate ensembles by deriving their analytical formulae.
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Scribner, Jennifer L., Eric A. Vance, David S. W. Protter, William M. Sheeran, Elliott Saslow, Ryan T. Cameron, Eric M. Klein, Jessica C. Jimenez, Mazen A. Kheirbek, and Zoe R. Donaldson. "A neuronal signature for monogamous reunion." Proceedings of the National Academy of Sciences 117, no. 20 (May 7, 2020): 11076–84. http://dx.doi.org/10.1073/pnas.1917287117.
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Pair-bond formation depends vitally on neuromodulatory signaling within the nucleus accumbens, but the neuronal dynamics underlying this behavior remain unclear. Using 1-photon in vivo Ca2+ imaging in monogamous prairie voles, we found that pair bonding does not elicit differences in overall nucleus accumbens Ca2+ activity. Instead, we identified distinct ensembles of neurons in this region that are recruited during approach to either a partner or a novel vole. The partner-approach neuronal ensemble increased in size following bond formation, and differences in the size of approach ensembles for partner and novel voles predict bond strength. In contrast, neurons comprising departure ensembles do not change over time and are not correlated with bond strength, indicating that ensemble plasticity is specific to partner approach. Furthermore, the neurons comprising partner and novel-approach ensembles are nonoverlapping while departure ensembles are more overlapping than chance, which may reflect another key feature of approach ensembles. We posit that the features of the partner-approach ensemble and its expansion upon bond formation potentially make it a key neuronal substrate associated with bond formation and maturation.
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Hsu, Kuo-Wei. "A Theoretical Analysis of Why Hybrid Ensembles Work." Computational Intelligence and Neuroscience 2017 (2017): 1–12. http://dx.doi.org/10.1155/2017/1930702.
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Inspired by the group decision making process, ensembles or combinations of classifiers have been found favorable in a wide variety of application domains. Some researchers propose to use the mixture of two different types of classification algorithms to create a hybrid ensemble. Why does such an ensemble work? The question remains. Following the concept of diversity, which is one of the fundamental elements of the success of ensembles, we conduct a theoretical analysis of why hybrid ensembles work, connecting using different algorithms to accuracy gain. We also conduct experiments on classification performance of hybrid ensembles of classifiers created by decision tree and naïve Bayes classification algorithms, each of which is a top data mining algorithm and often used to create non-hybrid ensembles. Therefore, through this paper, we provide a complement to the theoretical foundation of creating and using hybrid ensembles.
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Čyplytė, Raminta. "The Interaction Among Lithuanian Folk Dance Ensembles in the Context of Cultural Education: Directors’ Attitude." Pedagogika 114, no. 2 (June 10, 2014): 200–208. http://dx.doi.org/10.15823/p.2014.017.
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The article aims to reveal features and expression of the interaction between the state song and dance ensemble “Lietuva” and folk dance ensembles of higher education institutions in the process of youth cultural education. Since this aspect has not been analyzed in detail, the research was held among directors of folk dance ensembles of higher education institutions and the state song and dance ensemble „Lietuva“ and attempted to reveal two perspectives.The questioning of the directors showed that the interaction between ensemble “Lietuva” and folk dance ensembles of high schools in the context of youth cultural education exists and appears through folk dance ensembles connecting factors such as: genre of folk dance, common cultural activities and repertoire as well as common content of education which includes teaching methods, dance technique and its evaluation, other problems and relevant topics which forces to attract attention to the peculiarity of folk dance and its promotion in the contemporary cultural context.Directors of the ensemble “Lietuva” and high school ensembles stated that the ensemble “Lietuva” is still relevant today and actively participate in the cultural education of young people through folk dance, song and music hereby preserving national traditions and customs.
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WINDEATT, T., and G. ARDESHIR. "DECISION TREE SIMPLIFICATION FOR CLASSIFIER ENSEMBLES." International Journal of Pattern Recognition and Artificial Intelligence 18, no. 05 (August 2004): 749–76. http://dx.doi.org/10.1142/s021800140400340x.
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The goal of designing an ensemble of simple classifiers is to improve the accuracy of a recognition system. However, the performance of ensemble methods is problem-dependent and the classifier learning algorithm has an important influence on ensemble performance. In particular, base classifiers that are too complex may result in overfitting. In this paper, the performance of Bagging, Boosting and Error-Correcting Output Code (ECOC) is compared for five decision tree pruning methods. A description is given for each of the pruning methods and the ensemble techniques. AdaBoost.OC which is a combination of Boosting and ECOC is compared with the pseudo-loss based version of Boosting, AdaBoost.M2 and the influence of pruning on the performance of the ensembles is studied. Motivated by the result that both pruned and unpruned ensembles made by AdaBoost.OC give similar accuracy, pruned ensembles are compared with ensembles of Decision Stumps. This leads to the hypothesis that ensembles of simple classifiers may give better performance for some problems. Using the application of face recognition, it is shown that an AdaBoost.OC ensemble of Decision Stumps outperforms an ensemble of pruned C4.5 trees for face identification, but is inferior for face verification. The implication is that in some real-world tasks to achieve best accuracy of an ensemble, it may be necessary to select base classifier complexity.
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Hart, Emma, and Kevin Sim. "On Constructing Ensembles for Combinatorial Optimisation." Evolutionary Computation 26, no. 1 (March 2018): 67–87. http://dx.doi.org/10.1162/evco_a_00203.
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Although the use of ensemble methods in machine-learning is ubiquitous due to their proven ability to outperform their constituent algorithms, ensembles of optimisation algorithms have received relatively little attention. Existing approaches lag behind machine-learning in both theory and practice, with no principled design guidelines available. In this article, we address fundamental questions regarding ensemble composition in optimisation using the domain of bin-packing as an example. In particular, we investigate the trade-off between accuracy and diversity, and whether diversity metrics can be used as a proxy for constructing an ensemble, proposing a number of novel metrics for comparing algorithm diversity. We find that randomly composed ensembles can outperform ensembles of high-performing algorithms under certain conditions and that judicious choice of diversity metric is required to construct good ensembles. The method and findings can be generalised to any metaheuristic ensemble, and lead to better understanding of how to undertake principled ensemble design.
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Liu, Li Min, and Xiao Ping Fan. "A Survey: Clustering Ensemble Selection." Advanced Materials Research 403-408 (November 2011): 2760–63. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.2760.
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Traditional clustering ensemble combines all of the available clustering partitions to get the final clustering result. But in supervised classification area,it has been known that selective classifier ensembles can always achieve better solutions.Following the selective classifier ensembles,the question of clustering ensemble is defined as clustering ensemble selection.The paper introduces the concept of clustering ensemble selection and gives the survey of clustering ensemble selection algorithms.
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Kumar, Gulshan, and Krishan Kumar. "The Use of Artificial-Intelligence-Based Ensembles for Intrusion Detection: A Review." Applied Computational Intelligence and Soft Computing 2012 (2012): 1–20. http://dx.doi.org/10.1155/2012/850160.
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In supervised learning-based classification, ensembles have been successfully employed to different application domains. In the literature, many researchers have proposed different ensembles by considering different combination methods, training datasets, base classifiers, and many other factors. Artificial-intelligence-(AI-) based techniques play prominent role in development of ensemble for intrusion detection (ID) and have many benefits over other techniques. However, there is no comprehensive review of ensembles in general and AI-based ensembles for ID to examine and understand their current research status to solve the ID problem. Here, an updated review of ensembles and their taxonomies has been presented in general. The paper also presents the updated review of various AI-based ensembles for ID (in particular) during last decade. The related studies of AI-based ensembles are compared by set of evaluation metrics driven from (1) architecture & approach followed; (2) different methods utilized in different phases of ensemble learning; (3) other measures used to evaluate classification performance of the ensembles. The paper also provides the future directions of the research in this area. The paper will help the better understanding of different directions in which research of ensembles has been done in general and specifically: field of intrusion detection systems (IDSs).
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Siegert, Stefan, Jochen Bröcker, and Holger Kantz. "Rank Histograms of Stratified Monte Carlo Ensembles." Monthly Weather Review 140, no. 5 (May 1, 2012): 1558–71. http://dx.doi.org/10.1175/mwr-d-11-00302.1.
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Abstract The application of forecast ensembles to probabilistic weather prediction has spurred considerable interest in their evaluation. Such ensembles are commonly interpreted as Monte Carlo ensembles meaning that the ensemble members are perceived as random draws from a distribution. Under this interpretation, a reasonable property to ask for is statistical consistency, which demands that the ensemble members and the verification behave like draws from the same distribution. A widely used technique to assess statistical consistency of a historical dataset is the rank histogram, which uses as a criterion the number of times that the verification falls between pairs of members of the ordered ensemble. Ensemble evaluation is rendered more specific by stratification, which means that ensembles that satisfy a certain condition (e.g., a certain meteorological regime) are evaluated separately. Fundamental relationships between Monte Carlo ensembles, their rank histograms, and random sampling from the probability simplex according to the Dirichlet distribution are pointed out. Furthermore, the possible benefits and complications of ensemble stratification are discussed. The main conclusion is that a stratified Monte Carlo ensemble might appear inconsistent with the verification even though the original (unstratified) ensemble is consistent. The apparent inconsistency is merely a result of stratification. Stratified rank histograms are thus not necessarily flat. This result is demonstrated by perfect ensemble simulations and supplemented by mathematical arguments. Possible methods to avoid or remove artifacts that stratification induces in the rank histogram are suggested.
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Fraley, Chris, Adrian E. Raftery, and Tilmann Gneiting. "Calibrating Multimodel Forecast Ensembles with Exchangeable and Missing Members Using Bayesian Model Averaging." Monthly Weather Review 138, no. 1 (January 1, 2010): 190–202. http://dx.doi.org/10.1175/2009mwr3046.1.
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Abstract Bayesian model averaging (BMA) is a statistical postprocessing technique that generates calibrated and sharp predictive probability density functions (PDFs) from forecast ensembles. It represents the predictive PDF as a weighted average of PDFs centered on the bias-corrected ensemble members, where the weights reflect the relative skill of the individual members over a training period. This work adapts the BMA approach to situations that arise frequently in practice; namely, when one or more of the member forecasts are exchangeable, and when there are missing ensemble members. Exchangeable members differ in random perturbations only, such as the members of bred ensembles, singular vector ensembles, or ensemble Kalman filter systems. Accounting for exchangeability simplifies the BMA approach, in that the BMA weights and the parameters of the component PDFs can be assumed to be equal within each exchangeable group. With these adaptations, BMA can be applied to postprocess multimodel ensembles of any composition. In experiments with surface temperature and quantitative precipitation forecasts from the University of Washington mesoscale ensemble and ensemble Kalman filter systems over the Pacific Northwest, the proposed extensions yield good results. The BMA method is robust to exchangeability assumptions, and the BMA postprocessed combined ensemble shows better verification results than any of the individual, raw, or BMA postprocessed ensemble systems. These results suggest that statistically postprocessed multimodel ensembles can outperform individual ensemble systems, even in cases in which one of the constituent systems is superior to the others.
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Schwartz, Craig S. "Medium-Range Convection-Allowing Ensemble Forecasts with a Variable-Resolution Global Model." Monthly Weather Review 147, no. 8 (July 31, 2019): 2997–3023. http://dx.doi.org/10.1175/mwr-d-18-0452.1.
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Abstract Two sets of global, 132-h (5.5-day), 10-member ensemble forecasts were produced with the Model for Prediction Across Scales (MPAS) for 35 cases in April and May 2017. One MPAS ensemble had a quasi-uniform 15-km mesh while the other employed a variable-resolution mesh with 3-km cell spacing over the conterminous United States (CONUS) that smoothly relaxed to 15 km over the rest of the globe. Precipitation forecasts from both MPAS ensembles were objectively verified over the central and eastern CONUS to assess the potential benefits of configuring MPAS with a 3-km mesh refinement region for medium-range forecasts. In addition, forecasts from NCEP’s operational Global Ensemble Forecast System were evaluated and served as a baseline against which to compare the experimental MPAS ensembles. The 3-km MPAS ensemble most faithfully reproduced the observed diurnal cycle of precipitation throughout the 132-h forecasts and had superior precipitation skill and reliability over the first 48 h. However, after 48 h, the three ensembles had more similar spread, reliability, and skill, and differences between probabilistic precipitation forecasts derived from the 3- and 15-km MPAS ensembles were typically statistically insignificant. Nonetheless, despite fewer benefits of increased resolution for spatial placement after 48 h, 3-km ensemble members explicitly provided potentially valuable guidance regarding convective mode throughout the 132-h forecasts while the other ensembles did not. Collectively, these results suggest both strengths and limitations of medium-range high-resolution ensemble forecasts and reveal pathways for future investigations to improve understanding of high-resolution global ensembles with variable-resolution meshes.
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Berrocal, Veronica J., Adrian E. Raftery, and Tilmann Gneiting. "Combining Spatial Statistical and Ensemble Information in Probabilistic Weather Forecasts." Monthly Weather Review 135, no. 4 (April 1, 2007): 1386–402. http://dx.doi.org/10.1175/mwr3341.1.
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Abstract Forecast ensembles typically show a spread–skill relationship, but they are also often underdispersive, and therefore uncalibrated. Bayesian model averaging (BMA) is a statistical postprocessing method for forecast ensembles that generates calibrated probabilistic forecast products for weather quantities at individual sites. This paper introduces the spatial BMA technique, which combines BMA and the geostatistical output perturbation (GOP) method, and extends BMA to generate calibrated probabilistic forecasts of whole weather fields simultaneously, rather than just weather events at individual locations. At any site individually, spatial BMA reduces to the original BMA technique. The spatial BMA method provides statistical ensembles of weather field forecasts that take the spatial structure of observed fields into account and honor the flow-dependent information contained in the dynamical ensemble. The members of the spatial BMA ensemble are obtained by dressing the weather field forecasts from the dynamical ensemble with simulated spatially correlated error fields, in proportions that correspond to the BMA weights for the member models in the dynamical ensemble. Statistical ensembles of any size can be generated at minimal computational cost. The spatial BMA technique was applied to 48-h forecasts of surface temperature over the Pacific Northwest in 2004, using the University of Washington mesoscale ensemble. The spatial BMA ensemble generally outperformed the BMA and GOP ensembles and showed much better verification results than the raw ensemble, both at individual sites, for weather field forecasts, and for forecasts of composite quantities, such as average temperature in National Weather Service forecast zones and minimum temperature along the Interstate 90 Mountains to Sound Greenway.
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Kioutsioukis, I., and S. Galmarini. "<i>De praeceptis ferendis</i>: good practice in multi-model ensembles." Atmospheric Chemistry and Physics 14, no. 21 (November 11, 2014): 11791–815. http://dx.doi.org/10.5194/acp-14-11791-2014.
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Abstract. Ensembles of air quality models have been formally and empirically shown to outperform single models in many cases. Evidence suggests that ensemble error is reduced when the members form a diverse and accurate ensemble. Diversity and accuracy are hence two factors that should be taken care of while designing ensembles in order for them to provide better predictions. Theoretical aspects like the bias–variance–covariance decomposition and the accuracy–diversity decomposition are linked together and support the importance of creating ensemble that incorporates both these elements. Hence, the common practice of unconditional averaging of models without prior manipulation limits the advantages of ensemble averaging. We demonstrate the importance of ensemble accuracy and diversity through an inter-comparison of ensemble products for which a sound mathematical framework exists, and provide specific recommendations for model selection and weighting for multi-model ensembles. The sophisticated ensemble averaging techniques, following proper training, were shown to have higher skill across all distribution bins compared to solely ensemble averaging forecasts.
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Codo, Mayra, and Miguel A. Rico-Ramirez. "Ensemble Radar-Based Rainfall Forecasts for Urban Hydrological Applications." Geosciences 8, no. 8 (August 7, 2018): 297. http://dx.doi.org/10.3390/geosciences8080297.
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Radar rainfall forecasting is of major importance to predict flows in the sewer system to enhance early flood warning systems in urban areas. In this context, reducing radar rainfall estimation uncertainties can improve rainfall forecasts. This study utilises an ensemble generator that assesses radar rainfall uncertainties based on historical rain gauge data as ground truth. The ensemble generator is used to produce probabilistic radar rainfall forecasts (radar ensembles). The radar rainfall forecast ensembles are compared against a stochastic ensemble generator. The rainfall forecasts are used to predict sewer flows in a small urban area in the north of England using an Infoworks CS model. Uncertainties in radar rainfall forecasts are assessed using relative operating characteristic (ROC) curves, and the results showed that the radar ensembles overperform the stochastic ensemble generator in the first hour of the forecasts. The forecast predictability is however rapidly lost after 30 min lead-time. This implies that knowledge of the statistical properties of the radar rainfall errors can help to produce more meaningful radar rainfall forecast ensembles.
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Allen, Douglas R., Karl W. Hoppel, and David D. Kuhl. "Hybrid ensemble 4DVar assimilation of stratospheric ozone using a global shallow water model." Atmospheric Chemistry and Physics 16, no. 13 (July 7, 2016): 8193–204. http://dx.doi.org/10.5194/acp-16-8193-2016.
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Abstract. Wind extraction from stratospheric ozone (O3) assimilation is examined using a hybrid ensemble 4-D variational assimilation (4DVar) shallow water model (SWM) system coupled to the tracer advection equation. Stratospheric radiance observations are simulated using global observations of the SWM fluid height (Z), while O3 observations represent sampling by a typical polar-orbiting satellite. Four ensemble sizes were examined (25, 50, 100, and 1518 members), with the largest ensemble equal to the number of dynamical state variables. The optimal length scale for ensemble localization was found by tuning an ensemble Kalman filter (EnKF). This scale was then used for localizing the ensemble covariances that were blended with conventional covariances in the hybrid 4DVar experiments. Both optimal length scale and optimal blending coefficient increase with ensemble size, with optimal blending coefficients varying from 0.2–0.5 for small ensembles to 0.5–1.0 for large ensembles. The hybrid system outperforms conventional 4DVar for all ensemble sizes, while for large ensembles the hybrid produces similar results to the offline EnKF. Assimilating O3 in addition to Z benefits the winds in the hybrid system, with the fractional improvement in global vector wind increasing from ∼ 35 % with 25 and 50 members to ∼ 50 % with 1518 members. For the smallest ensembles (25 and 50 members), the hybrid 4DVar assimilation improves the zonal wind analysis over conventional 4DVar in the Northern Hemisphere (winter-like) region and also at the Equator, where Z observations alone have difficulty constraining winds due to lack of geostrophy. For larger ensembles (100 and 1518 members), the hybrid system results in both zonal and meridional wind error reductions, relative to 4DVar, across the globe.
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Larikova, Liudmyla. "Women’s Bandura Ensembles. Aspects of Working with Small Vocal Ensembles." Artistic Culture Topical Issues, no. 18(1) (May 31, 2022): 37–42. http://dx.doi.org/10.31500/1992-5514.18(1).2022.260413.
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The paper describes the specific features in the functioning of women’s bandura ensembles. In particular, it gives a historiographical picture of vocal-instrumental bandura performance, describes the stages of academisation of women’ssinging in bandura ensembles, and overviews the main methodological approaches of work with women’s vocal-instrumental bandura ensembles. Women’s bandura ensembles as an academic genre trend of Ukrainian musical culture have three fields of study: vocal and instrumental (performers), genre and stylistic (composers), and social and educational (cultural). Also, it is important to pay due attention to the widening of theoretical knowledge and practical skills, and to serious work on the methods of improving the performing and expressing components of singing and playing bandura in the ensemble.
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Roebber, Paul J. "Evolving Ensembles." Monthly Weather Review 143, no. 2 (February 1, 2015): 471–90. http://dx.doi.org/10.1175/mwr-d-14-00058.1.
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Abstract An ensemble forecast method using evolutionary programming, including various forms of genetic exchange, disease, mutation, and the training of solutions within ecological niches, is presented. A 2344-member ensemble generated in this way is tested for 60-h minimum temperature forecasts for Chicago, Illinois. The ensemble forecasts are superior in both ensemble average root-mean-square error and Brier skill score to those obtained from a 21-member operational ensemble model output statistics (MOS) forecast. While both ensembles are underdispersive, spread calibration produces greater gains in probabilistic skill for the evolutionary program ensemble than for the MOS ensemble. When a Bayesian model combination calibration is used, the skill advantage for the evolutionary program ensemble relative to the MOS ensemble increases for root-mean-square error, but decreases for Brier skill score. Further improvement in root-mean-square error is obtained when the raw evolutionary program and MOS forecasts are pooled, and a new Bayesian model combination ensemble is produced. Future extensions to the method are discussed, including those capable of producing more complex forms, those involving 1000-fold increases in training populations, and adaptive methods.
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LaRow, T. E., S. D. Cocke, and D. W. Shin. "Multiconvective Parameterizations as a Multimodel Proxy for Seasonal Climate Studies." Journal of Climate 18, no. 15 (August 1, 2005): 2963–78. http://dx.doi.org/10.1175/jcli3448.1.
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Abstract A six-member multicoupled model ensemble is created by using six state-of-the-art deep atmospheric convective schemes. The six convective schemes are used inside a single model and make up the ensemble. This six-member ensemble is compared against a multianalysis ensemble, which is created by varying the initial start dates of the atmospheric component of the coupled model. Both ensembles were integrated for seven months (November–May) over a 12-yr period from 1987 to 1998. Examination of the sea surface temperature and precipitation show that while deterministic skill scores are slightly better for the multicoupled model ensemble the probabilistic skill scores favor the multimodel approach. Combining the two ensembles to create a larger ensemble size increases the probabilistic skill score compared to the multimodel. This altering physics approach to create a multimodel ensemble is seen as an easy way for small modeling centers to generate ensembles with better reliability than by only varying the initial conditions.
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Broomhead, Paul. "Individual Expressive Performance: Its Relationship to Ensemble Achievement, Technical Achievement, and Musical Background." Journal of Research in Music Education 49, no. 1 (April 2001): 71–84. http://dx.doi.org/10.2307/3345811.
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Participation in an expressive ensemble may be inappropriately presumed to produce expressive independence in individual ensemble members. This study is an examination of relationships between individual expressive achievement and (a) the expressive achievement of choral ensembles, (b) technical performance, and (c) musical background. Subjects included 11 high school choral ensembles and 82 individual ensemble members. A multivariate analysis of variance (MANOVA) revealed no significant relationships between individual and ensemble expressive achievement. Cor-relations showed technical and expressive performance to be strongly related. Significantly related musical background factors from a MANOVA included: (a) involvement in outside performing groups, (b) semesters of high school choir, (c) private vocal lessons, and (d) age of first private lessons. The study provided grounds for questioning the assumption that expressive ensembles yield expressive individuals.
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Li, Peijing, Yun Su, Qianqian Huang, Jun Li, and Jingxian Xu. "Experimental study on the thermal regulation performance of winter uniform used for high school students." Textile Research Journal 89, no. 12 (July 31, 2018): 2316–29. http://dx.doi.org/10.1177/0040517518790977.
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To understand the effectiveness of some garment adjustment designs for high school uniform in winter, manikin tests and subjective wear trials were carried out. Five series of school uniform ensembles were involved in the experiments. They were further collocated into 17 ensemble configurations with detachable designs (ensembles A and B) and opening structures (ensembles C, D, and E). As manikin test results showed, the thermal insulation of ensembles A, B and C varied most significantly due to their adjustment design. The possible thermal insulation regulation levels were approximately 68% and 80% for ensembles A and B, and 60% and 90% for ensemble C. Two human trials that simulated students’ daily movements between indoor and outdoor classes were conducted with ensemble A. Two climate chambers were used at the same time for indoor and outdoor environment simulation. In Case X, where ensemble A was assumed to be non-detachable, skin temperatures that were 0.6℃ lower were finally observed compared to Case Y, where ensemble A was detachable. Moreover, significantly ( p < 0.1) better thermal comfort and thermal sensation evaluations were given during low-intensity activities in Case Y, especially for the torso segments. The detachable high school uniform design was finally proved to be efficient in improving human thermal comfort under various class environments. It was also concluded that more protective measures should be adopted for the hands and face in the school uniform design process.
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HAYASHI, YOICHI. "NEURAL NETWORK RULE EXTRACTION BY A NEW ENSEMBLE CONCEPT AND ITS THEORETICAL AND HISTORICAL BACKGROUND: A REVIEW." International Journal of Computational Intelligence and Applications 12, no. 04 (December 2013): 1340006. http://dx.doi.org/10.1142/s1469026813400063.
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This paper presents theoretical and historical backgrounds related to neural network rule extraction. It also investigates approaches for neural network rule extraction by ensemble concepts. Bologna pointed out that although many authors had generated comprehensive models from individual networks, much less work had been done to explain ensembles of neural networks. This paper carefully surveyed the previous work on rule extraction from neural network ensembles since 1988. We are aware of three major research groups i.e., Bologna' group, Zhou' group and Hayashi' group. The reason of these situations is obvious. Since the structures of previous neural network ensembles were quite complicated, the research on the efficient rule extraction algorithm from neural network ensembles was few although their learning capability was extremely high. Thus, these issues make rule extraction algorithm for neural network ensemble difficult task. However, there is a practical need for new ideas for neural network ensembles in order to realize the extremely high-performance needs of various rule extraction problems in real life. This paper successively explain nature of artificial neural networks, origin of neural network rule extraction, incorporating fuzziness in neural network rule extraction, theoretical foundation of neural network rule extraction, computational complexity of neural network rule extraction, neuro-fuzzy hybridization, previous rule extraction from neural network ensembles and difficulties of previous neural network ensembles. Next, this paper address three principles of proposed neural network rule extraction: to increase recognition rates, to extract rules from neural network ensembles, and to minimize the use of computing resources. We also propose an ensemble-recursive-rule extraction (E-Re-RX) by two or three standard backpropagation to train multi-layer perceptrons (MLPs), which enabled extremely high recognition accuracy and the extraction of comprehensible rules. Furthermore, this enabled rule extraction that resulted in fewer rules than those in previously proposed methods. This paper summarizes experimental results of rule extraction using E-Re-RX by multiple standard backpropagation MLPs and provides deep discussions. The results make it possible for the output from a neural network ensemble to be in the form of rules, thus open the "black box" of trained neural networks ensembles. Finally, we provide valuable conclusions and as future work, three open questions on the E-Re-RX algorithm.
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Parvin, Hamid, Hamid Alinejad-Rokny, and Sajad Parvin. "A Classifier Ensemble of Binary Classifier Ensembles." International Journal of Learning Management Systems 1, no. 2 (July 1, 2013): 37–47. http://dx.doi.org/10.12785/ijlms/010204.
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KIM, Y., W. STREET, and F. MENCZER. "Optimal ensemble construction via meta-evolutionary ensembles." Expert Systems with Applications 30, no. 4 (May 2006): 705–14. http://dx.doi.org/10.1016/j.eswa.2005.07.030.
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Niu, Yiping, Guodong Ren, Giulia Lin, Letizia Di Biase, and Simone Fattorini. "Fine-Scale Vegetation Characteristics Drive Insect Ensemble Structures in a Desert Ecosystem: The Tenebrionid Beetles (Coleoptera: Tenebrionidae) Inhabiting the Ulan Buh Desert (Inner Mongolia, China)." Insects 11, no. 7 (July 2, 2020): 410. http://dx.doi.org/10.3390/insects11070410.
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In community ecology, ensembles are defined as phylogenetically bounded groups of species that use a similar set of resources within a community. Tenebrionids are a conspicuous faunal component of Asian deserts, but little is known about their community ecology. We investigated if tenebrionids associated with different plant species constitute ensembles with a different ecological structure. Sampling was done with pitfall traps placed beneath the most common plant species. Tenebrionid abundance patterns were modelled by fitting rank–abundance plots. The association between tenebrionid species and plant species was tested using contingency tables. Differences in ensemble diversity were investigated by diversity profiles. All ensembles were fitted by the geometric series model. Tenebrionid species were differently associated with different plant species. Diversity profiles indicate that different ensembles have different diversity patterns, because of differences in species relative abundance. Tenebrionids form different ensembles associated with the different dominant plant species. All these ensembles are, however, characterized by similar patterns of dominance, following the “niche pre-emption” model, and a steep decline in the diversity profiles. This indicates that similar environmental conditions lead to similar insect ensemble organization, although the most abundant species may vary, which suggests a role for microhabitat selection.
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Choi, Hee-Wook, Keunhee Han, and Chansoo Kim. "Probabilistic Forecast of Visibility at Gimpo, Incheon, and Jeju International Airports Using Weighted Model Averaging." Atmosphere 13, no. 12 (November 25, 2022): 1969. http://dx.doi.org/10.3390/atmos13121969.
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In this study, weighted model averaging (WMA) was applied to calibrating ensemble forecasts generated using Limited-area ENsemble prediction System (LENS). WMA is an easy-to-implement post-processing technique that assigns a greater weight to the ensemble member forecast that exhibits better performance; it is used to provide probabilistic visibility forecasting in the form of a predictive probability density function for ensembles. The predictive probability density function is a mixture of discrete point mass and two-sided truncated normal distribution components. Observations were obtained at Gimpo, Incheon, and Jeju International Airports, and 13 ensemble member forecasts were obtained using LENS, for the period of December 2018 to June 2019. Prior to applying WMA, a reliability analysis was conducted using rank histograms and reliability diagrams to identify the statistical consistency between the ensembles and the corresponding observations. The WMA method was then applied to each raw ensemble model, and a weighted predictive probability density function was proposed. Performances were evaluated using the mean absolute error, the continuous ranked probability score, the Brier score, and the probability integral transform. The results showed that the proposed method provided improved performance compared with the raw ensembles, indicating that the raw ensembles were well calibrated using the predicted probability density function.
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Franz, K. J., and T. S. Hogue. "Evaluating uncertainty estimates in hydrologic models: borrowing measures from the forecast verification community." Hydrology and Earth System Sciences Discussions 8, no. 2 (March 30, 2011): 3085–131. http://dx.doi.org/10.5194/hessd-8-3085-2011.
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Abstract. The hydrologic community is generally moving towards the use of probabilistic estimates of streamflow, primarily through the implementation of Ensemble Streamflow Prediction (ESP) systems, ensemble data assimilation methods, or multi-modeling platforms. However, evaluation of probabilistic outputs has not necessarily kept pace with ensemble generation. Much of the modeling community is still performing model evaluation using standard deterministic measures, such as error, correlation, or bias, typically applied to the ensemble mean or median. Probabilistic forecast verification methods have been well developed, particularly in the atmospheric sciences yet, few have been adopted for evaluating uncertainty estimates in hydrologic model simulations. In the current paper, we overview existing probabilistic forecast verification methods and apply the methods to evaluate and compare model ensembles produced from different parameter uncertainty estimation methods. The Generalized Uncertainty Likelihood Estimator (GLUE), a modified version of GLUE, and the Shuffle Complex Evolution Metropolis (SCEM) are used to generate model ensembles for the National Weather Service SACramento Soil Moisture Accounting (SAC-SMA) model for 12 forecast basins located in the Southeastern United States. We evaluate the model ensembles using relevant metrics in the following categories: distribution, correlation, accuracy, conditional statistics, and categorical statistics. We show that the probabilistic metrics are easily adapted to model simulation ensembles and provide a robust analysis of parameter uncertainty, one that is commensurate with the dimension of the ensembles themselves. Application of these methods requires no information in addition to what is already available as part of traditional model validation methodology and considers the entire ensemble or uncertainty range in the approach.
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Herzog, Rubén, Arturo Morales, Soraya Mora, Joaquín Araya, María-José Escobar, Adrian G. Palacios, and Rodrigo Cofré. "Scalable and accurate method for neuronal ensemble detection in spiking neural networks." PLOS ONE 16, no. 7 (July 30, 2021): e0251647. http://dx.doi.org/10.1371/journal.pone.0251647.
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We propose a novel, scalable, and accurate method for detecting neuronal ensembles from a population of spiking neurons. Our approach offers a simple yet powerful tool to study ensemble activity. It relies on clustering synchronous population activity (population vectors), allows the participation of neurons in different ensembles, has few parameters to tune and is computationally efficient. To validate the performance and generality of our method, we generated synthetic data, where we found that our method accurately detects neuronal ensembles for a wide range of simulation parameters. We found that our method outperforms current alternative methodologies. We used spike trains of retinal ganglion cells obtained from multi-electrode array recordings under a simple ON-OFF light stimulus to test our method. We found a consistent stimuli-evoked ensemble activity intermingled with spontaneously active ensembles and irregular activity. Our results suggest that the early visual system activity could be organized in distinguishable functional ensembles. We provide a Graphic User Interface, which facilitates the use of our method by the scientific community.
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Yamaguchi, Munehiko, Frédéric Vitart, Simon T. K. Lang, Linus Magnusson, Russell L. Elsberry, Grant Elliott, Masayuki Kyouda, and Tetsuo Nakazawa. "Global Distribution of the Skill of Tropical Cyclone Activity Forecasts on Short- to Medium-Range Time Scales." Weather and Forecasting 30, no. 6 (November 25, 2015): 1695–709. http://dx.doi.org/10.1175/waf-d-14-00136.1.
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Abstract Operational global medium-range ensemble forecasts of tropical cyclone (TC) activity (genesis plus the subsequent track) are systematically evaluated to understand the skill of the state-of-the-art ensembles in forecasting TC activity as well as the relative benefits of a multicenter grand ensemble with respect to a single-model ensemble. The global ECMWF, JMA, NCEP, and UKMO ensembles are evaluated from 2010 to 2013 in seven TC basins around the world. The verification metric is the Brier skill score (BSS), which is calculated within a 3-day time window over a forecast length of 2 weeks to examine the skill from short- to medium-range time scales (0–14 days). These operational global medium-range ensembles are capable of providing guidance on TC activity forecasts that extends into week 2. Multicenter grand ensembles (MCGEs) tend to have better forecast skill (larger BSSs) than does the best single-model ensemble, which is the ECMWF ensemble in most verification time windows and most TC basins. The relative benefit of the MCGEs is relatively large in the north Indian Ocean and TC basins in the Southern Hemisphere where the BSS of the single-model ensemble is relatively small. The BSS metric and the reliability are found to be sensitive to the choice of threshold wind values that are used to define the model TCs.
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Siegert, S., J. Bröcker, and H. Kantz. "On the predictability of outliers in ensemble forecasts." Advances in Science and Research 8, no. 1 (March 28, 2012): 53–57. http://dx.doi.org/10.5194/asr-8-53-2012.
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Abstract. In numerical weather prediction, ensembles are used to retrieve probabilistic forecasts of future weather conditions. We consider events where the verification is smaller than the smallest, or larger than the largest ensemble member of a scalar ensemble forecast. These events are called outliers. In a statistically consistent K-member ensemble, outliers should occur with a base rate of 2/(K+1). In operational ensembles this base rate tends to be higher. We study the predictability of outlier events in terms of the Brier Skill Score and find that forecast probabilities can be calculated which are more skillful than the unconditional base rate. This is shown analytically for statistically consistent ensembles. Using logistic regression, forecast probabilities for outlier events in an operational ensemble are calculated. These probabilities exhibit positive skill which is quantitatively similar to the analytical results. Possible causes of these results as well as their consequences for ensemble interpretation are discussed.
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Timme, Nicholas M., David Linsenbardt, and Christopher C. Lapish. "A Method to Present and Analyze Ensembles of Information Sources." Entropy 22, no. 5 (May 21, 2020): 580. http://dx.doi.org/10.3390/e22050580.
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Information theory is a powerful tool for analyzing complex systems. In many areas of neuroscience, it is now possible to gather data from large ensembles of neural variables (e.g., data from many neurons, genes, or voxels). The individual variables can be analyzed with information theory to provide estimates of information shared between variables (forming a network between variables), or between neural variables and other variables (e.g., behavior or sensory stimuli). However, it can be difficult to (1) evaluate if the ensemble is significantly different from what would be expected in a purely noisy system and (2) determine if two ensembles are different. Herein, we introduce relatively simple methods to address these problems by analyzing ensembles of information sources. We demonstrate how an ensemble built of mutual information connections can be compared to null surrogate data to determine if the ensemble is significantly different from noise. Next, we show how two ensembles can be compared using a randomization process to determine if the sources in one contain more information than the other. All code necessary to carry out these analyses and demonstrations are provided.
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Yokohata, Tokuta, Mark J. Webb, Matthew Collins, Keith D. Williams, Masakazu Yoshimori, Julia C. Hargreaves, and James D. Annan. "Structural Similarities and Differences in Climate Responses to CO2 Increase between Two Perturbed Physics Ensembles." Journal of Climate 23, no. 6 (March 15, 2010): 1392–410. http://dx.doi.org/10.1175/2009jcli2917.1.
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Abstract The equilibrium climate sensitivity (ECS) of the two perturbed physics ensembles (PPE) generated using structurally different GCMs, Model for Interdisciplinary Research on Climate (MIROC3.2) and the Third Hadley Centre Atmospheric Model with slab ocean (HadSM3), is investigated. A method to quantify the shortwave (SW) cloud feedback by clouds with different cloud-top pressure is developed. It is found that the difference in the ensemble means of the ECS between the two ensembles is mainly caused by differences in the SW low-level cloud feedback. The ensemble mean SW cloud feedback and ECS of the MIROC3.2 ensemble is larger than that of the HadSM3 ensemble. This is likely related to the 1XCO2 low-level cloud albedo of the former being larger than that of the latter. It is also found that the largest contribution to the within-ensemble variation of ECS comes from the SW low-level cloud feedback in both ensembles. The mechanism that causes the within-ensemble variation is different between the two ensembles. In the HadSM3 ensemble, members with large 1XCO2 low-level cloud albedo have large SW cloud feedback and large ECS; ensemble members with large 1XCO2 cloud cover have large negative SW cloud feedback and relatively low ECS. In the MIROC3.2 ensemble, the 1XCO2 low-level cloud albedo is much more tightly constrained, and no relationship is found between it and the cloud feedback. These results indicate that both the parametric uncertainties sampled in PPEs and the structural uncertainties of GCMs are important and worth further investigation.
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Witt, Jessica K., Benjamin A. Clegg, Christopher D. Wickens, C. A. P. Smith, Emily L. Laitin, and Amelia C. Warden. "Dynamic Ensembles versus Cones of Uncertainty: Visualizations to Support Understanding of Uncertainty in Hurricane Forecasts." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 64, no. 1 (December 2020): 1644–48. http://dx.doi.org/10.1177/1071181320641399.
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Visualizations attempt to convey the uncertain track of an approaching hurricane. The current experiment contrasted decision characteristics that resulted from observing hurricane paths presented using cones of uncertainty versus a new form of dynamic ensemble. Participants made judgments about whether to evacuate a town at different eccentricities to the central predicted path of a storm. Results showed that dynamic ensembles have different properties to cone displays. Presentations of dynamic ensembles encouraged greater consideration of evacuation at locations further from the most probable path, but that were still at risk. However, dynamic ensembles resulted in lower evacuation rates at the center of the distribution, consistent with a probabilistic sense of the risk but nonetheless a potentially undesirable strategy. In addition, perceptions of the evacuation need with dynamic ensemble presentations were more strongly influenced by the amount of variability than with cones. The implications for use of dynamic ensembles are discussed.
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Abdillah, Abid Famasya, Cornelius Bagus Purnama Putra, Apriantoni Apriantoni, Safitri Juanita, and Diana Purwitasari. "Ensemble-based Methods for Multi-label Classification on Biomedical Question-Answer Data." Journal of Information Systems Engineering and Business Intelligence 8, no. 1 (April 26, 2022): 42–50. http://dx.doi.org/10.20473/jisebi.8.1.42-50.
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Background: Question-answer (QA) is a popular method to seek health-related information and biomedical data. Such questions can refer to more than one medical entity (multi-label) so determining the correct tags is not easy. The question classification (QC) mechanism in a QA system can narrow down the answers we are seeking. Objective: This study develops a multi-label classification using the heterogeneous ensembles method to improve accuracy in biomedical data with long text dimensions. Methods: We used the ensemble method with heterogeneous deep learning and machine learning for multi-label extended text classification. There are 15 various single models consisting of three deep learning (CNN, LSTM, and BERT) and four machine learning algorithms (SVM, kNN, Decision Tree, and Naïve Bayes) with various text representations (TF-IDF, Word2Vec, and FastText). We used the bagging approach with a hard voting mechanism for the decision-making. Results: The result shows that deep learning is more powerful than machine learning as a single multi-label biomedical data classification method. Moreover, we found that top-three was the best number of base learners by combining the ensembles method. Heterogeneous-based ensembles with three learners resulted in an F1-score of 82.3%, which is better than the best single model by CNN with an F1-score of 80%. Conclusion: A multi-label classification of biomedical QA using ensemble models is better than single models. The result shows that heterogeneous ensembles are more potent than homogeneous ensembles on biomedical QA data with long text dimensions. Keywords: Biomedical Question Classification, Ensemble Method, Heterogeneous Ensembles, Multi-Label Classification, Question Answering
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Harrison, Scott D., Don Lebler, Gemma Carey, Matt Hitchcock, and Jessica O'Bryan. "Making music or gaining grades? Assessment practices in tertiary music ensembles." British Journal of Music Education 30, no. 1 (July 2, 2012): 27–42. http://dx.doi.org/10.1017/s0265051712000253.
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Participation in an ensemble is a significant aspect of tertiary music experience. Learning and assessment practices within ensembles have rarely been investigated in Australia and the perceptions of staff and students as to how they learn and are assessed within ensembles remain largely unexplored. This paper reports on part of a larger project that investigated learning and assessment practices within ensembles at an Australian Conservatorium of Music. Ensembles contribute to approximately 25% of student work in each semester, and the assessment contributes to a final grade for the semester. Using a case study methodology, four music ensembles were studied. The data generated were coded into themes including assessment practices and processes; collaborative learning practices; the development of the professional musician; and communication and transparency between participants and the institution. Findings revealed that both staff and student participants in this study perceived ensemble participation to be valuable to the development of a professional musician, but that assessment procedures did not always support this goal. Institutional demands were found to be an inhibiting factor in the assessment of ensembles, and both students and teachers had problems with current assessment procedures, resulting in confusion and lack of transparency about how ensembles are assessed. Approaches to the development of the professional musician became a dominant discussion point and a substantial finding of the research. By examining dominant and subjugated knowledge in this domain, institutional power relations were interrogated, existing practices were challenged, and assessment practices rethought.
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Junk, Constantin, Stephan Späth, Lueder von Bremen, and Luca Delle Monache. "Comparison and Combination of Regional and Global Ensemble Prediction Systems for Probabilistic Predictions of Hub-Height Wind Speed." Weather and Forecasting 30, no. 5 (October 1, 2015): 1234–53. http://dx.doi.org/10.1175/waf-d-15-0021.1.
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Abstract The objective of this paper is to compare probabilistic 100-m wind speed forecasts, which are relevant for wind energy applications, from different regional and global ensemble prediction systems (EPSs) at six measurement towers in central Europe and to evaluate the benefits of combining single-model ensembles into multimodel ensembles. The global 51-member EPS from the European Centre for Medium-Range Weather Forecasts (ECMWF EPS) is compared against the Consortium for Small-Scale Modelling’s (COSMO) limited-area 16-member EPS (COSMO-LEPS) and a regional, high-resolution 20-member EPS centered over Germany (COSMO-DE EPS). The ensemble forecasts are calibrated with univariate (wind speed) ensemble model output statistics (EMOS) and bivariate (wind vector) recursive and adaptive calibration (AUV). The multimodel ensembles are constructed by pooling together raw or best-calibrated ensemble forecasts. An additional postprocessing of these multimodel ensembles with both EMOS and AUV is also tested. The best-performing calibration methodology for ECMWF EPS is AUV, while EMOS performs better than AUV for the calibration of COSMO-DE EPS. COSMO-LEPS has similar skill when calibrated with both EMOS and AUV. The AUV ECMWF EPS outperforms the EMOS COSMO-LEPS and COSMO-DE EPS for deterministic and probabilistic wind speed forecast skill. For most thresholds, ECMWF EPS has a comparable reliability and sharpness but higher discrimination ability. Multimodel ensembles, which are constructed by pooling together the best-calibrated EPSs, improve the skill relative to the AUV ECMWF EPS. An analysis of the error correlation among the EPSs indicates that multimodel ensemble skill can be considerably higher when the error correlation is low.
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Volpe, Gualtiero, Alessandro D'Ausilio, Leonardo Badino, Antonio Camurri, and Luciano Fadiga. "Measuring social interaction in music ensembles." Philosophical Transactions of the Royal Society B: Biological Sciences 371, no. 1693 (May 5, 2016): 20150377. http://dx.doi.org/10.1098/rstb.2015.0377.
Full textAbstract:
Music ensembles are an ideal test-bed for quantitative analysis of social interaction. Music is an inherently social activity, and music ensembles offer a broad variety of scenarios which are particularly suitable for investigation. Small ensembles, such as string quartets, are deemed a significant example of self-managed teams, where all musicians contribute equally to a task. In bigger ensembles, such as orchestras, the relationship between a leader (the conductor) and a group of followers (the musicians) clearly emerges. This paper presents an overview of recent research on social interaction in music ensembles with a particular focus on (i) studies from cognitive neuroscience; and (ii) studies adopting a computational approach for carrying out automatic quantitative analysis of ensemble music performances.
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Lehmkuhle, M. J., R. A. Normann, and E. M. Maynard. "Trial-by-Trial Discrimination of Three Enantiomer Pairs by Neural Ensembles in Mammalian Olfactory Bulb." Journal of Neurophysiology 95, no. 3 (March 2006): 1369–79. http://dx.doi.org/10.1152/jn.01334.2004.
Full textAbstract:
Populations of output neurons in the mammalian olfactory bulb (OB) exhibit distinct, widespread spatial and temporal activation patterns when stimulated with odorants. However, questions remain as to how ensembles of mitral/tufted (M/T) neurons in the mammalian OB represent odorant information. In this report, the single-trial encoding limits of random ensembles of putative single- and multiunit M/T cells in the anesthetized rat OB during presentations of enantiomers of limonene, carvone, and 2-butanol are investigated using simultaneous multielectrode recording techniques. The results of these experiments are: the individual constituents of our recorded ensembles broadly represent information about the presented odorants, the ensemble single-trial response of small spatially distributed populations of M/T neurons can readily discriminate between six different odorants, and the most consistent odorant discrimination is attained when the ensemble consists of all available units and their responses are integrated over an entire breathing cycle. These results suggest that small differences in spike counts among the ensemble members become significant when taken within the context of the entire ensemble. This may explain how ensembles of broadly tuned OB neurons contribute to olfactory perception and may explain how small numbers of individual units receiving input from distinct olfactory receptor neurons can be combined to form a robust representation of odorants.