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Journal articles on the topic 'Ensemble'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Lawrence, Andrew R., and James A. Hansen. "A Transformed Lagged Ensemble Forecasting Technique for Increasing Ensemble Size." Monthly Weather Review 135, no. 4 (2007): 1424–38. http://dx.doi.org/10.1175/mwr3357.1.

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Abstract An ensemble-based data assimilation approach is used to transform old ensemble forecast perturbations with more recent observations for the purpose of inexpensively increasing ensemble size. The impact of the transformations are propagated forward in time over the ensemble’s forecast period without rerunning any models, and these transformed ensemble forecast perturbations can be combined with the most recent ensemble forecast to sensibly increase forecast ensemble sizes. Because the transform takes place in perturbation space, the transformed perturbations must be centered on the ens

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Rodríguez Molina, Javier. "Variantes léxicas y gramaticales del adverbio ensemble en la documentación medieval." Cuadernos del Instituto Historia de la Lengua, no. 7 (January 16, 2023): 405–24. http://dx.doi.org/10.58576/cilengua.vi7.129.

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En este artículo se estudia la evolución histórica del adverbiomedieval ensemble, que apenas si ha recibido atención en labibliografía. A partir de un exhaustivo análisis de documentos notariales,se defiende que ensemble no es un préstamo del francés antiguo,como sugieren varios estudios previos, sino un desarrollovernáculo del latín peninsular ĬN SĬMŬL. Esta hipótesis se basa tantoen la distribución geográfica de los datos medievales como en la variaciónmorfológica que presenta el adverbio (ensemble, ensembra,ensembla). En la Edad Media ensemble presenta un patrón de distribucióndialectal cla

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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 (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 performan

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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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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

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6

Saqib, Malik, and Sharma Narendra. "A Vast Review of Recognizing the Presence of Android Malware Based on Ensemble Machine Learning Technique." Indian Journal of Science and Technology 17, no. 2 (2024): 149–65. https://doi.org/10.17485/IJST/v17i2.2406.

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Abstract <strong>Background:</strong> It is evaluated that there is 70% to 80% of smartphone users have an Android mobile. Given its trend, a lot of malware strikes on the Android OS. In 2018, the largest number of malware attacks was identified, when there were 10.5 billion such malicious activity detected worldwide. Machine learning has emerged as a promising approach for detecting Android malware, and Ensemble machine learning has been shown to enhance the accuracy of malware detection in other domains. Objectives: In this paper, the systematic literature review were conducted using na

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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 (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 stud

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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 (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

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9

Alazba, Amal, and Hamoud Aljamaan. "Software Defect Prediction Using Stacking Generalization of Optimized Tree-Based Ensembles." Applied Sciences 12, no. 9 (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 hyperparam

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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 (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 slig

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Petrus Geraldino Resha Almi, Hubertus Lado Lewar, Alfonsia Maria Resaa, Maria Silvani Kupu Da, and Agustinus Renaldus Afoan Elu. "Pelatihan Ansambel Musik bagi Siswa – siswi Kelas X dan XI SMA Katolik Giovanni Kupang." Pandawa : Pusat Publikasi Hasil Pengabdian Masyarakat 3, no. 1 (2025): 112–19. https://doi.org/10.61132/pandawa.v3i1.1618.

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Ensemble music is a musical presentation consisting of a mixture of several musical instruments that contain rhythmic, melodic, and harmonious elements. Etymologically, the word ensemble comes from the French word ensemble which means group. An ensemble is also known as a musical ensemble that plays one or several types of musical instruments. In education at school, ensembles have a fairly important role in improving the quality of students' music. In addition to gaining theoretical ensemble knowledge, students are also directed to practice musical ensembles, so that they can channel their cr

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Louk, Maya Hilda Lestari, and Bayu Adhi Tama. "Exploring Ensemble-Based Class Imbalance Learners for Intrusion Detection in Industrial Control Networks." Big Data and Cognitive Computing 5, no. 4 (2021): 72. http://dx.doi.org/10.3390/bdcc5040072.

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Classifier ensembles have been utilized in the industrial cybersecurity sector for many years. However, their efficacy and reliability for intrusion detection systems remain questionable in current research, owing to the particularly imbalanced data issue. The purpose of this article is to address a gap in the literature by illustrating the benefits of ensemble-based models for identifying threats and attacks in a cyber-physical power grid. We provide a framework that compares nine cost-sensitive individual and ensemble models designed specifically for handling imbalanced data, including cost-

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Dey, Seonaid R. A., Giovanni Leoncini, Nigel M. Roberts, Robert S. Plant, and Stefano Migliorini. "A Spatial View of Ensemble Spread in Convection Permitting Ensembles." Monthly Weather Review 142, no. 11 (2014): 4091–107. http://dx.doi.org/10.1175/mwr-d-14-00172.1.

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Abstract With movement toward kilometer-scale ensembles, new techniques are needed for their characterization. A new methodology is presented for detailed spatial ensemble characterization using the fractions skill score (FSS). To evaluate spatial forecast differences, the average and standard deviation are taken of the FSS calculated over all ensemble member–member pairs at different scales and lead times. These methods were found to give important information about the ensemble behavior allowing the identification of useful spatial scales, spinup times for the model, and upscale growth of er

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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 (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

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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 (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

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Siegert, Stefan, Jochen Bröcker, and Holger Kantz. "Rank Histograms of Stratified Monte Carlo Ensembles." Monthly Weather Review 140, no. 5 (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 a

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17

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 (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. Exchang

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18

Melhauser, Christopher, Fuqing Zhang, Yonghui Weng, Yi Jin, Hao Jin, and Qingyun Zhao. "A Multiple-Model Convection-Permitting Ensemble Examination of the Probabilistic Prediction of Tropical Cyclones: Hurricanes Sandy (2012) and Edouard (2014)." Weather and Forecasting 32, no. 2 (2017): 665–88. http://dx.doi.org/10.1175/waf-d-16-0082.1.

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Abstract This study examines a multimodel comparison of regional-scale convection-permitting ensembles including both physics and initial condition uncertainties for the probabilistic prediction of Hurricanes Sandy (2012) and Edouard (2014). The model cores examined include COAMPS-TC, HWRF, and WRF-ARW. Two stochastic physics schemes were also applied using the WRF-ARW model. Each ensemble was initialized with the same initial condition uncertainties represented by the analysis perturbations from a WRF-ARW-based real-time cycling ensemble Kalman filter. It is found that single-core ensembles w

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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 (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 combi

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20

Imran, Sheik, and Pradeep N. "A Review on Ensemble Machine and Deep Learning Techniques Used in the Classification of Computed Tomography Medical Images." International Journal of Health Sciences and Research 14, no. 1 (2024): 201–13. http://dx.doi.org/10.52403/ijhsr.20240124.

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Ensemble learning combines multiple base models to enhance predictive performance and generalize better on unseen data. In the context of Computed Tomography (CT) image processing, ensemble techniques often leverage diverse machine learning or deep learning architectures to achieve the best results. Ensemble machine learning and deep learning techniques have revolutionized the field of CT image processing by significantly improving accuracy, robustness, and efficiency in various medical imaging tasks. These methods have been instrumental in tasks such as image reconstruction, segmentation, cla

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Hart, Emma, and Kevin Sim. "On Constructing Ensembles for Combinatorial Optimisation." Evolutionary Computation 26, no. 1 (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 ca

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Yokohata, Tokuta, Mark J. Webb, Matthew Collins, et al. "Structural Similarities and Differences in Climate Responses to CO2 Increase between Two Perturbed Physics Ensembles." Journal of Climate 23, no. 6 (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

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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 (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.n

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Roberts, Brett, Burkely T. Gallo, Israel L. Jirak, et al. "What Does a Convection-Allowing Ensemble of Opportunity Buy Us in Forecasting Thunderstorms?" Weather and Forecasting 35, no. 6 (2020): 2293–316. http://dx.doi.org/10.1175/waf-d-20-0069.1.

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AbstractThe High Resolution Ensemble Forecast v2.1 (HREFv2.1), an operational convection-allowing model (CAM) ensemble, is an “ensemble of opportunity” wherein forecasts from several independently designed deterministic CAMs are aggregated and postprocessed together. Multiple dimensions of diversity in the HREFv2.1 ensemble membership contribute to ensemble spread, including model core, physics parameterization schemes, initial conditions (ICs), and time lagging. In this study, HREFv2.1 forecasts are compared against the High Resolution Rapid Refresh Ensemble (HRRRE) and the Multiscale data As

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Kim, Kue Bum, Hyun-Han Kwon, and Dawei Han. "Precipitation ensembles conforming to natural variations derived from a regional climate model using a new bias correction scheme." Hydrology and Earth System Sciences 20, no. 5 (2016): 2019–34. http://dx.doi.org/10.5194/hess-20-2019-2016.

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Abstract. This study presents a novel bias correction scheme for regional climate model (RCM) precipitation ensembles. A primary advantage of using model ensembles for climate change impact studies is that the uncertainties associated with the systematic error can be quantified through the ensemble spread. Currently, however, most of the conventional bias correction methods adjust all the ensemble members to one reference observation. As a result, the ensemble spread is degraded during bias correction. Since the observation is only one case of many possible realizations due to the climate natu

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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 (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

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Kioutsioukis, I., and S. Galmarini. "<i>De praeceptis ferendis</i>: good practice in multi-model ensembles." Atmospheric Chemistry and Physics Discussions 14, no. 11 (2014): 15803–65. http://dx.doi.org/10.5194/acpd-14-15803-2014.

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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. There exists a trade-off between diversity and accuracy for which one cannot be gained without expenses of the other. Theoretical aspects like the bias-variance-covariance decomposition and the accuracy-diversity decompos

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Radanovics, Sabine, Jean-Philippe Vidal, and Eric Sauquet. "Spatial Verification of Ensemble Precipitation: An Ensemble Version of SAL." Weather and Forecasting 33, no. 4 (2018): 1001–20. http://dx.doi.org/10.1175/waf-d-17-0162.1.

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Abstract Spatial verification methods able to handle high-resolution ensemble forecasts and analysis ensembles are increasingly required because of the increasing development of such ensembles. An ensemble extension of the structure–amplitude–location (SAL) spatial verification method is proposed here. The ensemble SAL (eSAL) allows for verifying ensemble forecasts against a deterministic or ensemble analysis. The eSAL components are equal to those of SAL in the deterministic case, thus allowing the comparison of deterministic and ensemble forecasts. The Mesoscale Verification Intercomparison

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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 (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 conte

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Mitchell, Herschel L., and P. L. Houtekamer. "Ensemble Kalman Filter Configurations and Their Performance with the Logistic Map." Monthly Weather Review 137, no. 12 (2009): 4325–43. http://dx.doi.org/10.1175/2009mwr2823.1.

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Abstract This paper examines ensemble Kalman filter (EnKF) performance for a number of different EnKF configurations. The study is performed in a perfect-model context using the logistic map as forecast model. The focus is on EnKF performance when the ensemble is small. In accordance with theory, it is found that those configurations that maintain an appropriate ensemble spread are indeed those with the smallest ensemble mean error in a data assimilation cycle. Thus, the deficient ensemble spread produced by the single-ensemble EnKF results in increased ensemble mean error for this configurati

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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 (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 i

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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 (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

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Wilks, D. S. "On the Reliability of the Rank Histogram." Monthly Weather Review 139, no. 1 (2011): 311–16. http://dx.doi.org/10.1175/2010mwr3446.1.

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Abstract Ensemble consistency is a name for the condition that an observation being forecast by a dynamical ensemble is statistically indistinguishable from the ensemble members. This statistical indistinguishability condition is meaningful only in a multivariate sense. That is, it pertains to the joint distribution of the ensemble members and the observation. The rank histogram has been designed to assess overall ensemble consistency, but mistakenly employing it to assess only restricted aspects of this joint distribution (e.g., the climatological distribution) leads to the incorrect conclusi

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Đurasević, Marko, and Domagoj Jakobović. "Heuristic Ensemble Construction Methods of Automatically Designed Dispatching Rules for the Unrelated Machines Environment." Axioms 13, no. 1 (2024): 37. http://dx.doi.org/10.3390/axioms13010037.

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Dynamic scheduling represents an important class of combinatorial optimisation problems that are usually solved with simple heuristics, the so-called dispatching rules (DRs). Designing efficient DRs is a tedious task, which is why it has been automated through the application of genetic programming (GP). Various approaches have been used to improve the results of automatically generated DRs, with ensemble learning being one of the best-known. The goal of ensemble learning is to create sets of automatically designed DRs that perform better together. One of the main problems in ensemble learning

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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 (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 calculat

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Buizza, Roberto. "Comparison of a 51-Member Low-Resolution (TL399L62) Ensemble with a 6-Member High-Resolution (TL799L91) Lagged-Forecast Ensemble." Monthly Weather Review 136, no. 9 (2008): 3343–62. http://dx.doi.org/10.1175/2008mwr2430.1.

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Abstract The 51-member TL399L62 ECMWF ensemble prediction system (EPS51) is compared with a lagged ensemble system based on the six most recent ECMWF TL799L91 forecasts (LAG6). The EPS51 and LAG6 systems are compared to two 6-member ensembles with a “weighted” ensemble-mean: EPS6wEM and LAG6wEM. EPS6wEM includes six members of EPS51 and has the ensemble mean constructed giving optimal weights to its members, while LAG6wEM includes the LAG6 six members and has the ensemble mean constructed giving optimal weights to its members. In these weighted ensembles, the optimal weights are based on 50-da

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Lee, Jared A., Walter C. Kolczynski, Tyler C. McCandless, and Sue Ellen Haupt. "An Objective Methodology for Configuring and Down-Selecting an NWP Ensemble for Low-Level Wind Prediction." Monthly Weather Review 140, no. 7 (2012): 2270–86. http://dx.doi.org/10.1175/mwr-d-11-00065.1.

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Abstract Ensembles of numerical weather prediction (NWP) model predictions are used for a variety of forecasting applications. Such ensembles quantify the uncertainty of the prediction because the spread in the ensemble predictions is correlated to forecast uncertainty. For atmospheric transport and dispersion and wind energy applications in particular, the NWP ensemble spread should accurately represent uncertainty in the low-level mean wind. To adequately sample the probability density function (PDF) of the forecast atmospheric state, it is necessary to account for several sources of uncerta

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Jian, Wang. "Ensemble singing as a phenomenon of Western European music: historical discourse." Problems of Interaction Between Arts, Pedagogy and the Theory and Practice of Education 74, no. 74 (2025): 212–30. https://doi.org/10.34064/khnum1-74.10.

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Statement of the problem. In the modern musical practice, the vocal ensemble remains an important genre used in a concert performance, professional music education, and amateur circles. Understanding its historical development helps to better understand the performing ensemble tradition and adapt it to modern conditions. The vocal ensemble as a fundamental form of performing art occupies an important place in the world musical culture. Its emergence, development, and transformation in different historical periods reflected not only musical and stylistic changes, but also deep socio-cultural pr

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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 (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 ex

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Buehner, Mark. "Local Ensemble Transform Kalman Filter with Cross Validation." Monthly Weather Review 148, no. 6 (2020): 2265–82. http://dx.doi.org/10.1175/mwr-d-19-0402.1.

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Abstract Many ensemble data assimilation (DA) approaches suffer from the so-called inbreeding problem. As a consequence, there is an excessive reduction in ensemble spread by the DA procedure, causing the analysis ensemble spread to systematically underestimate the uncertainty of the ensemble mean analysis. The stochastic EnKF used for operational NWP in Canada largely avoids this problem by applying cross validation, that is, using an independent subset of ensemble members for updating each member. The goal of the present study is to evaluate two new variations of the local ensemble transform

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Alipova, Kseniya A., Vasiliy G. Mizyak, Mikhail A. Tolstykh, and Gordey S. Goyman. "Stochastic perturbations in the semi-Lagrangian advection algorithm of the SL-AV global atmosphere model." Russian Journal of Numerical Analysis and Mathematical Modelling 39, no. 1 (2024): 1–11. http://dx.doi.org/10.1515/rnam-2024-0001.

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Abstract An algorithm for stochastic perturbation of the semi-Lagrangian trajectories is implemented in the ensemble weather prediction system based on the global atmosphere model SL-AV20 with a horizontal resolution of approximately 20 km, 51 vertical levels, and Local Ensemble Transform Kalman Filter (LETKF). The combined use of methods for stochastic perturbation of trajectories and the parameters and tendencies of subgrid-scale processes parameterizations allows to generate ensembles with a larger spread compared to ensembles without stochastic perturbations of trajectories. An improvement

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Sun, Xiao Wei, and Hong Bo Zhou. "Research on Applied Technology in Experiments with Three Boosting Algorithms." Advanced Materials Research 908 (March 2014): 513–16. http://dx.doi.org/10.4028/www.scientific.net/amr.908.513.

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Boosting algorithms are a means of building a strong ensemble classifier by aggregating a sequence of weak hypotheses. An ensemble consists of a set of independently trained classifiers whose predictions are combined when classifying novel instances. Previous research has shown that an ensemble as a whole is often more accurate than any of the single classifiers in the ensemble. In this paper we use applied technology to built an ensemble using a voting methodology of Boosting-BAN and Boosting-MultiTAN ensembles with 10 sub-classifiers in each one. We performed a comparison with Boosting-BAN a

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Yamaguchi, Munehiko, Frédéric Vitart, Simon T. K. Lang, et al. "Global Distribution of the Skill of Tropical Cyclone Activity Forecasts on Short- to Medium-Range Time Scales." Weather and Forecasting 30, no. 6 (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 we

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Yussouf, Nusrat, Jidong Gao, David J. Stensrud, and Guoqing Ge. "The Impact of Mesoscale Environmental Uncertainty on the Prediction of a Tornadic Supercell Storm Using Ensemble Data Assimilation Approach." Advances in Meteorology 2013 (2013): 1–15. http://dx.doi.org/10.1155/2013/731647.

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Numerical experiments over the past years indicate that incorporating environmental variability is crucial for successful very short-range convective-scale forecasts. To explore the impact of model physics on the creation of environmental variability and its uncertainty, combined mesoscale-convective scale data assimilation experiments are conducted for a tornadic supercell storm. Two 36-member WRF-ARW model-based mesoscale EAKF experiments are conducted to provide background environments using either fixed or multiple physics schemes across the ensemble members. Two 36-member convective-scale

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Baker, Casey M., and Yiyang Gong. "Identifying properties of pattern completion neurons in a computational model of the visual cortex." PLOS Computational Biology 19, no. 6 (2023): e1011167. http://dx.doi.org/10.1371/journal.pcbi.1011167.

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Neural ensembles are found throughout the brain and are believed to underlie diverse cognitive functions including memory and perception. Methods to activate ensembles precisely, reliably, and quickly are needed to further study the ensembles’ role in cognitive processes. Previous work has found that ensembles in layer 2/3 of the visual cortex (V1) exhibited pattern completion properties: ensembles containing tens of neurons were activated by stimulation of just two neurons. However, methods that identify pattern completion neurons are underdeveloped. In this study, we optimized the selection

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Jiang, Xiangkui, Chang-an Wu, and Huaping Guo. "Forest Pruning Based on Branch Importance." Computational Intelligence and Neuroscience 2017 (2017): 1–11. http://dx.doi.org/10.1155/2017/3162571.

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A forest is an ensemble with decision trees as members. This paper proposes a novel strategy to pruning forest to enhance ensemble generalization ability and reduce ensemble size. Unlike conventional ensemble pruning approaches, the proposed method tries to evaluate the importance of branches of trees with respect to the whole ensemble using a novel proposed metric called importance gain. The importance of a branch is designed by considering ensemble accuracy and the diversity of ensemble members, and thus the metric reasonably evaluates how much improvement of the ensemble accuracy can be ach

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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 (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 wi

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Ahmad, Amir, Hamza Abujabal, and C. Aswani Kumar. "Random Subclasses Ensembles by Using 1-Nearest Neighbor Framework." International Journal of Pattern Recognition and Artificial Intelligence 31, no. 10 (2017): 1750031. http://dx.doi.org/10.1142/s0218001417500318.

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A classifier ensemble is a combination of diverse and accurate classifiers. Generally, a classifier ensemble performs better than any single classifier in the ensemble. Naive Bayes classifiers are simple but popular classifiers for many applications. As it is difficult to create diverse naive Bayes classifiers, naive Bayes ensembles are not very successful. In this paper, we propose Random Subclasses (RS) ensembles for Naive Bayes classifiers. In the proposed method, new subclasses for each class are created by using 1-Nearest Neighbor (1-NN) framework that uses randomly selected points from t

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Fujita, Tadashi, David J. Stensrud, and David C. Dowell. "Surface Data Assimilation Using an Ensemble Kalman Filter Approach with Initial Condition and Model Physics Uncertainties." Monthly Weather Review 135, no. 5 (2007): 1846–68. http://dx.doi.org/10.1175/mwr3391.1.

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Abstract The assimilation of surface observations using an ensemble Kalman filter (EnKF) approach is evaluated for the potential to improve short-range forecasting. Two severe weather cases are examined, in which the assimilation is performed over a 6-h period using hourly surface observations followed by an 18-h simulation period. Ensembles are created in three different ways—by using different initial and boundary conditions, by using different model physical process schemes, and by using both different initial and boundary conditions and different model physical process schemes. The ensembl

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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 (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

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