Journal articles: 'Bootstrapping neural networks'

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Franke, Jürgen, and Michael H. Neumann. "Bootstrapping Neural Networks." Neural Computation 12, no. 8 (August 1, 2000): 1929–49. http://dx.doi.org/10.1162/089976600300015204.

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Knowledge about the distribution of a statistical estimator is important for various purposes, such as the construction of confidence intervals for model parameters or the determination of critical values of tests. A widely used method to estimate this distribution is the so-called bootstrap, which is based on an imitation of the probabilistic structure of the data-generating process on the basis of the information provided by a given set of random observations. In this article we investigate this classical method in the context of artificial neural networks used for estimating a mapping from input to output space. We establish consistency results for bootstrap estimates of the distribution of parameter estimates.

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Li, Xiangsheng, Yanghui Rao, Haoran Xie, Raymond Yiu Keung Lau, Jian Yin, and Fu Lee Wang. "Bootstrapping Social Emotion Classification with Semantically Rich Hybrid Neural Networks." IEEE Transactions on Affective Computing 8, no. 4 (October 1, 2017): 428–42. http://dx.doi.org/10.1109/taffc.2017.2716930.

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Mistry, Sajib, Lie Qu, and Athman Bouguettaya. "Layer-based Composite Reputation Bootstrapping." ACM Transactions on Internet Technology 22, no. 1 (February 28, 2022): 1–28. http://dx.doi.org/10.1145/3448610.

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We propose a novel generic reputation bootstrapping framework for composite services. Multiple reputation-related indicators are considered in a layer-based framework to implicitly reflect the reputation of the component services. The importance of an indicator on the future performance of a component service is learned using a modified Random Forest algorithm. We propose a topology-aware Forest Deep Neural Network (fDNN) to find the correlations between the reputation of a composite service and reputation indicators of component services. The trained fDNN model predicts the reputation of a new composite service with the confidence value. Experimental results with real-world dataset prove the efficiency of the proposed approach.

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Álvarez-Aparicio, Claudia, Ángel Manuel Guerrero-Higueras, Luis V. Calderita, Francisco J. Rodríguez-Lera, Vicente Matellán, and Camino Fernández-Llamas. "Convolutional Neural Networks Refitting by Bootstrapping for Tracking People in a Mobile Robot." Applied Sciences 11, no. 21 (October 27, 2021): 10043. http://dx.doi.org/10.3390/app112110043.

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Convolutional Neural Networks are usually fitted with manually labelled data. The labelling process is very time-consuming since large datasets are required. The use of external hardware may help in some cases, but it also introduces noise to the labelled data. In this paper, we pose a new data labelling approach by using bootstrapping to increase the accuracy of the PeTra tool. PeTra allows a mobile robot to estimate people’s location in its environment by using a LIDAR sensor and a Convolutional Neural Network. PeTra has some limitations in specific situations, such as scenarios where there are not any people. We propose to use the actual PeTra release to label the LIDAR data used to fit the Convolutional Neural Network. We have evaluated the resulting system by comparing it with the previous one—where LIDAR data were labelled with a Real Time Location System. The new release increases the MCC-score by 65.97%.

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Yan, Yilin, Min Chen, Saad Sadiq, and Mei-Ling Shyu. "Efficient Imbalanced Multimedia Concept Retrieval by Deep Learning on Spark Clusters." International Journal of Multimedia Data Engineering and Management 8, no. 1 (January 2017): 1–20. http://dx.doi.org/10.4018/ijmdem.2017010101.

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The classification of imbalanced datasets has recently attracted significant attention due to its implications in several real-world use cases. The classifiers developed on datasets with skewed distributions tend to favor the majority classes and are biased against the minority class. Despite extensive research interests, imbalanced data classification remains a challenge in data mining research, especially for multimedia data. Our attempt to overcome this hurdle is to develop a convolutional neural network (CNN) based deep learning solution integrated with a bootstrapping technique. Considering that convolutional neural networks are very computationally expensive coupled with big training datasets, we propose to extract features from pre-trained convolutional neural network models and feed those features to another full connected neutral network. Spark implementation shows promising performance of our model in handling big datasets with respect to feasibility and scalability.

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Barth, R., J. IJsselmuiden, J. Hemming, and E. J. Van Henten. "Synthetic bootstrapping of convolutional neural networks for semantic plant part segmentation." Computers and Electronics in Agriculture 161 (June 2019): 291–304. http://dx.doi.org/10.1016/j.compag.2017.11.040.

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Hsiao, Hsiao-Fen, Jiang-Chuan Huang, and Zheng-Wei Lin. "Portfolio construction using bootstrapping neural networks: evidence from global stock market." Review of Derivatives Research 23, no. 3 (July 25, 2019): 227–47. http://dx.doi.org/10.1007/s11147-019-09163-y.

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Richman, Ronald, and Mario V. Wüthrich. "Nagging Predictors." Risks 8, no. 3 (August 4, 2020): 83. http://dx.doi.org/10.3390/risks8030083.

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We define the nagging predictor, which, instead of using bootstrapping to produce a series of i.i.d. predictors, exploits the randomness of neural network calibrations to provide a more stable and accurate predictor than is available from a single neural network run. Convergence results for the family of Tweedie’s compound Poisson models, which are usually used for general insurance pricing, are provided. In the context of a French motor third-party liability insurance example, the nagging predictor achieves stability at portfolio level after about 20 runs. At an insurance policy level, we show that for some policies up to 400 neural network runs are required to achieve stability. Since working with 400 neural networks is impractical, we calibrate two meta models to the nagging predictor, one unweighted, and one using the coefficient of variation of the nagging predictor as a weight, finding that these latter meta networks can approximate the nagging predictor well, only with a small loss of accuracy.

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KUAN, MEI MING, CHEE PENG LIM, and ROBERT F. HARRISON. "ON OPERATING STRATEGIES OF THE FUZZY ARTMAP NEURAL NETWORK: A COMPARATIVE STUDY." International Journal of Computational Intelligence and Applications 03, no. 01 (March 2003): 23–43. http://dx.doi.org/10.1142/s1469026803000847.

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In this paper, the effectiveness of three different operating strategies applied to the Fuzzy ARTMAP (FAM) neural network in pattern classification tasks is analyzed and compared. Three types of FAM, namely average FAM, voting FAM, and ordered FAM, are formed for experimentation. In average FAM, a pool of the FAM networks is trained using random sequences of input patterns, and the performance metrics from multiple networks are averaged. In voting FAM, predictions from a number of FAM networks are combined using the majority-voting scheme to reach a final output. In ordered FAM, a pre-processing procedure known as the ordering algorithm is employed to identify a fixed sequence of input patterns for training the FAM network. Three medical data sets are employed to evaluate the performances of these three types of FAM. The results are analyzed and compared with those from other learning systems. Bootstrapping has also been used to analyze and quantify the results statistically.

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Medina, Oded, Roi Yozevitch, and Nir Shvalb. "Synthetic Sensor Array Training Sets for Neural Networks." Journal of Sensors 2019 (September 10, 2019): 1–10. http://dx.doi.org/10.1155/2019/9254315.

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It is often hard to relate the sensor’s electrical output to the physical scenario when a multidimensional measurement is of interest. An artificial neural network may be a solution. Nevertheless, if the training data set is extracted from a real experimental setup, it can become unreachable in terms of time resources. The same issue arises when the physical measurement is expected to extend across a wide range of values. This paper presents a novel method for overcoming the long training time in a physical experiment set up by bootstrapping a relatively small data set for generating a synthetic data set which can be used for training an artificial neural network. Such a method can be applied to various measurement systems that yield sensor output which combines simultaneous occurrences or wide-range values of physical phenomena of interest. We discuss to which systems our method may be applied. We exemplify our results on three study cases: a seismic sensor array, a linear array of strain gauges, and an optical sensor array. We present the experimental process, its results, and the resulting accuracies.

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Schnürch, Simon, and Ralf Korn. "POINT AND INTERVAL FORECASTS OF DEATH RATES USING NEURAL NETWORKS." ASTIN Bulletin 52, no. 1 (December 3, 2021): 333–60. http://dx.doi.org/10.1017/asb.2021.34.

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AbstractThe Lee–Carter model has become a benchmark in stochastic mortality modeling. However, its forecasting performance can be significantly improved upon by modern machine learning techniques. We propose a convolutional neural network (NN) architecture for mortality rate forecasting, empirically compare this model as well as other NN models to the Lee–Carter model and find that lower forecast errors are achievable for many countries in the Human Mortality Database. We provide details on the errors and forecasts of our model to make it more understandable and, thus, more trustworthy. As NN by default only yield point estimates, previous works applying them to mortality modeling have not investigated prediction uncertainty. We address this gap in the literature by implementing a bootstrapping-based technique and demonstrate that it yields highly reliable prediction intervals for our NN model.

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Krč, Rostislav, Jan Podroužek, Martina Kratochvílová, Ivan Vukušič, and Otto Plášek. "Neural Network-Based Train Identification in Railway Switches and Crossings Using Accelerometer Data." Journal of Advanced Transportation 2020 (November 24, 2020): 1–10. http://dx.doi.org/10.1155/2020/8841810.

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This paper aims to analyse possibilities of train type identification in railway switches and crossings (S&C) based on accelerometer data by using contemporary machine learning methods such as neural networks. That is a unique approach since trains have been only identified in a straight track. Accelerometer sensors placed around the S&C structure were the source of input data for subsequent models. Data from four S&C at different locations were considered and various neural network architectures evaluated. The research indicated the feasibility to identify trains in S&C using neural networks from accelerometer data. Models trained at one location are generally transferable to another location despite differences in geometrical parameters, substructure, and direction of passing trains. Other challenges include small dataset and speed variation of the trains that must be considered for accurate identification. Results are obtained using statistical bootstrapping and are presented in a form of confusion matrices.

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La Rocca, Michele, and Cira Perna. "Opening the Black Box: Bootstrapping Sensitivity Measures in Neural Networks for Interpretable Machine Learning." Stats 5, no. 2 (April 25, 2022): 440–57. http://dx.doi.org/10.3390/stats5020026.

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Artificial neural networks are powerful tools for data analysis, particularly in the context of highly nonlinear regression models. However, their utility is critically limited due to the lack of interpretation of the model given its black-box nature. To partially address the problem, the paper focuses on the important problem of feature selection. It proposes and discusses a statistical test procedure for selecting a set of input variables that are relevant to the model while taking into account the multiple testing nature of the problem. The approach is within the general framework of sensitivity analysis and uses the conditional expectation of functions of the partial derivatives of the output with respect to the inputs as a sensitivity measure. The proposed procedure extensively uses the bootstrap to approximate the test statistic distribution under the null while controlling the familywise error rate to correct for data snooping arising from multiple testing. In particular, a pair bootstrap scheme was implemented in order to obtain consistent results when using misspecified statistical models, a typical characteristic of neural networks. Numerical examples and a Monte Carlo simulation were carried out to verify the ability of the proposed test procedure to correctly identify the set of relevant features.

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La Rocca, Michele, and Cira Perna. "Opening the Black Box: Bootstrapping Sensitivity Measures in Neural Networks for Interpretable Machine Learning." Stats 5, no. 2 (April 25, 2022): 440–57. http://dx.doi.org/10.3390/stats5020026.

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Artificial neural networks are powerful tools for data analysis, particularly in the context of highly nonlinear regression models. However, their utility is critically limited due to the lack of interpretation of the model given its black-box nature. To partially address the problem, the paper focuses on the important problem of feature selection. It proposes and discusses a statistical test procedure for selecting a set of input variables that are relevant to the model while taking into account the multiple testing nature of the problem. The approach is within the general framework of sensitivity analysis and uses the conditional expectation of functions of the partial derivatives of the output with respect to the inputs as a sensitivity measure. The proposed procedure extensively uses the bootstrap to approximate the test statistic distribution under the null while controlling the familywise error rate to correct for data snooping arising from multiple testing. In particular, a pair bootstrap scheme was implemented in order to obtain consistent results when using misspecified statistical models, a typical characteristic of neural networks. Numerical examples and a Monte Carlo simulation were carried out to verify the ability of the proposed test procedure to correctly identify the set of relevant features.

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POEL, MANNES, and TACO EKKEL. "ANALYZING INFANT CRIES USING A COMMITTEE OF NEURAL NETWORKS IN ORDER TO DETECT HYPOXIA RELATED DISORDER." International Journal on Artificial Intelligence Tools 15, no. 03 (June 2006): 397–410. http://dx.doi.org/10.1142/s0218213006002734.

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Based on the hypothesis that the sound of the infant cry contains information on the infant's health status, research has been done on how to improve classification of neonate crying sounds into categories called 'normal' and 'abnormal' - the latter referring to some hypoxia-related disorder. Research in this field is hindered by lack of test cases and limited understanding of feature relevance. The research described here combines various ways of dealing with the small data set problem. First, feature pre-selection is done using sequential backwards elimination of possible combinations where the performance of the set of features is tested by a Probabilistic Neural Network which has the advantage of fast learning. Using these features a neural network committee, consisting of Radial Basis Function Neural Networks, was trained on the data, using bootstrapping. This construction yields a multi-classifier system with an overall classification performance of 85% on the so-called "All Cry Units" (ACU) data set, an increase of 34% with respect to the a priori probability of 51%. Several leave-1-out experiments for Linear Discriminant Analysis (LDA), Support Vector Machines (SVM) and Neural Networks (NN) have been conducted in order to compare the performance of the multi-classifier system.

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Avola, Danilo, Marco Bernardi, Luigi Cinque, Cristiano Massaroni, and Gian Luca Foresti. "Fusing Self-Organized Neural Network and Keypoint Clustering for Localized Real-Time Background Subtraction." International Journal of Neural Systems 30, no. 04 (March 2, 2020): 2050016. http://dx.doi.org/10.1142/s0129065720500161.

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Moving object detection in video streams plays a key role in many computer vision applications. In particular, separation between background and foreground items represents a main prerequisite to carry out more complex tasks, such as object classification, vehicle tracking, and person re-identification. Despite the progress made in recent years, a main challenge of moving object detection still regards the management of dynamic aspects, including bootstrapping and illumination changes. In addition, the recent widespread of Pan–Tilt–Zoom (PTZ) cameras has made the management of these aspects even more complex in terms of performance due to their mixed movements (i.e. pan, tilt, and zoom). In this paper, a combined keypoint clustering and neural background subtraction method, based on Self-Organized Neural Network (SONN), for real-time moving object detection in video sequences acquired by PTZ cameras is proposed. Initially, the method performs a spatio-temporal tracking of the sets of moving keypoints to recognize the foreground areas and to establish the background. Then, it adopts a neural background subtraction, localized in these areas, to accomplish a foreground detection able to manage bootstrapping and gradual illumination changes. Experimental results on three well-known public datasets, and comparisons with different key works of the current literature, show the efficiency of the proposed method in terms of modeling and background subtraction.

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Lian, Cheng, Lingzi Zhu, Zhigang Zeng, Yixin Su, Wei Yao, and Huiming Tang. "Constructing prediction intervals for landslide displacement using bootstrapping random vector functional link networks selective ensemble with neural networks switched." Neurocomputing 291 (May 2018): 1–10. http://dx.doi.org/10.1016/j.neucom.2018.02.046.

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de Diego, José A., Jakub Nadolny, Ángel Bongiovanni, Jordi Cepa, Mirjana Pović, Ana María Pérez García, Carmen P. Padilla Torres, et al. "Galaxy classification: deep learning on the OTELO and COSMOS databases." Astronomy & Astrophysics 638 (June 2020): A134. http://dx.doi.org/10.1051/0004-6361/202037697.

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Context. The accurate classification of hundreds of thousands of galaxies observed in modern deep surveys is imperative if we want to understand the universe and its evolution. Aims. Here, we report the use of machine learning techniques to classify early- and late-type galaxies in the OTELO and COSMOS databases using optical and infrared photometry and available shape parameters: either the Sérsic index or the concentration index. Methods. We used three classification methods for the OTELO database: (1) u − r color separation, (2) linear discriminant analysis using u − r and a shape parameter classification, and (3) a deep neural network using the r magnitude, several colors, and a shape parameter. We analyzed the performance of each method by sample bootstrapping and tested the performance of our neural network architecture using COSMOS data. Results. The accuracy achieved by the deep neural network is greater than that of the other classification methods, and it can also operate with missing data. Our neural network architecture is able to classify both OTELO and COSMOS datasets regardless of small differences in the photometric bands used in each catalog. Conclusions. In this study we show that the use of deep neural networks is a robust method to mine the cataloged data.

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Kearney-Ramos, Tonisha E., Jennifer S. Fausett, Jennifer L. Gess, Ashley Reno, Jennifer Peraza, Clint D. Kilts, and G. Andrew James. "Merging Clinical Neuropsychology and Functional Neuroimaging to Evaluate the Construct Validity and Neural Network Engagement of the n-Back Task." Journal of the International Neuropsychological Society 20, no. 7 (June 25, 2014): 736–50. http://dx.doi.org/10.1017/s135561771400054x.

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AbstractThe n-back task is a widely used neuroimaging paradigm for studying the neural basis of working memory (WM); however, its neuropsychometric properties have received little empirical investigation. The present study merged clinical neuropsychology and functional magnetic resonance imaging (fMRI) to explore the construct validity of the letter variant of the n-back task (LNB) and to further identify the task-evoked networks involved in WM. Construct validity of the LNB task was investigated using a bootstrapping approach to correlate LNB task performance across clinically validated neuropsychological measures of WM to establish convergent validity, as well as measures of related but distinct cognitive constructs (i.e., attention and short-term memory) to establish discriminant validity. Independent component analysis (ICA) identified brain networks active during the LNB task in 34 healthy control participants, and general linear modeling determined task-relatedness of these networks. Bootstrap correlation analyses revealed moderate to high correlations among measures expected to converge with LNB (|ρ|≥0.37) and weak correlations among measures expected to discriminate (|ρ|≤0.29), controlling for age and education. ICA identified 35 independent networks, 17 of which demonstrated engagement significantly related to task condition, controlling for reaction time variability. Of these, the bilateral frontoparietal networks, bilateral dorsolateral prefrontal cortices, bilateral superior parietal lobules including precuneus, and frontoinsular network were preferentially recruited by the 2-back condition compared to 0-back control condition, indicating WM involvement. These results support the use of the LNB as a measure of WM and confirm its use in probing the network-level neural correlates of WM processing. (JINS, 2014, 20, 1–15)

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Krč, Rostislav, Martina Kratochvílová, Jan Podroužek, Tomáš Apeltauer, Václav Stupka, and Tomáš Pitner. "Machine Learning-Based Node Characterization for Smart Grid Demand Response Flexibility Assessment." Sustainability 13, no. 5 (March 9, 2021): 2954. http://dx.doi.org/10.3390/su13052954.

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As energy distribution systems evolve from a traditional hierarchical load structure towards distributed smart grids, flexibility is increasingly investigated as both a key measure and core challenge of grid balancing. This paper contributes to the theoretical framework for quantifying network flexibility potential by introducing a machine learning based node characterization. In particular, artificial neural networks are considered for classification of historic demand data from several network substations. Performance of the resulting classifiers is evaluated with respect to clustering analysis and parameter space of the models considered, while the bootstrapping based statistical evaluation is reported in terms of mean confusion matrices. The resulting meta-models of individual nodes can be further utilized on a network level to mitigate the difficulties associated with identifying, implementing and actuating many small sources of energy flexibility, compared to the few large ones traditionally acknowledged.

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Gao, Tianyu, Xu Han, Ruobing Xie, Zhiyuan Liu, Fen Lin, Leyu Lin, and Maosong Sun. "Neural Snowball for Few-Shot Relation Learning." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (April 3, 2020): 7772–79. http://dx.doi.org/10.1609/aaai.v34i05.6281.

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Knowledge graphs typically undergo open-ended growth of new relations. This cannot be well handled by relation extraction that focuses on pre-defined relations with sufficient training data. To address new relations with few-shot instances, we propose a novel bootstrapping approach, Neural Snowball, to learn new relations by transferring semantic knowledge about existing relations. More specifically, we use Relational Siamese Networks (RSN) to learn the metric of relational similarities between instances based on existing relations and their labeled data. Afterwards, given a new relation and its few-shot instances, we use RSN to accumulate reliable instances from unlabeled corpora; these instances are used to train a relation classifier, which can further identify new facts of the new relation. The process is conducted iteratively like a snowball. Experiments show that our model can gather high-quality instances for better few-shot relation learning and achieves significant improvement compared to baselines. Codes and datasets are released on https://github.com/thunlp/Neural-Snowball.

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Hamzeh, S., M. Hajeb, S. K. Alavipanah, and J. Verrelst. "RETRIEVAL OF SUGARCANE LEAF AREA INDEX FROM PRISMA HYPERSPECTRAL DATA." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences X-4/W1-2022 (January 13, 2023): 271–77. http://dx.doi.org/10.5194/isprs-annals-x-4-w1-2022-271-2023.

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Abstract. The PRecursore IperSpettrale della Missione Applicativa (PRISMA) satellite of the Italian Space Agency, lunched in 2019, has provided a new generation source of hyperspectral data showing to have high potential in vegetation variable retrieval. In this study, the newly available PRISMA spectra were exploited to retrieve Leaf Area Index (LAI) of sugarcane using a new kind of Artificial Neural Networks (ANN) so-called Bayesian Regularized Artificial Neural Network (BRANN). The suggested BRANN retrieval model was implemented over a dataset collected during a field campaign in Amir Kabir Sugarcane Agro-Industrial zone, Khuzestan, Iran, in 2020. Principle Component Analysis (PCA) was utilized to reduce the dimensionality of PRISMA data cube. An accuracy assessment based on the bootstrapping procedure indicated RMSE of 0.67 m2/m2 for the LAI retrieval by applying the BRANN model. This study is a confirmation of the high performance of the BRANN method and high potential of PRISMA images to retrieve sugarcane LAI.

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Liu, Long. "Sports Video Motion Direction Detection and Target Tracking Algorithm Based on Convolutional Neural Network." Wireless Communications and Mobile Computing 2022 (July 11, 2022): 1–10. http://dx.doi.org/10.1155/2022/5760758.

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In order to effectively detect and monitor athletes and record various motion data of targets, the study suggests a study of target tracking algorithms to detect the direction of motion video sports movement based on the neural network. A class of feedforward neural networks with convolutional computation and deep structure is one of the representative algorithms of deep learning. Firstly, the athlete image is obtained from the video frame; combined with the nonathlete image to construct the training set, use the bootstrapping algorithm to train the convolutional neural network classifier. In the case of input picture frames, pyramids of different scales are then constructed by subsampling, and the location of many candidate athletes is detected by a neural network of disruption. Finally, these centers calculate the center of gravity of the athletes, find the athlete to represent the candidate, and determine the location of the final athlete through a local search process. The results of the experiment show that the proposed scheme of 6000 frames in the two game videos is compared with the AdaBoost scheme, and the detection rate of the proposed scheme is 75.41% to calculate the average detection accuracy and false alarm speed of all players. The detection rate is higher than the AdaBoost scheme. Therefore, this scheme has a high detection rate and low false positives.

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Johnson, Neil, Sameer Prasad, Amin Vahedian, Nezih Altay, and Ashish Jain. "Modelling ragpickers’ productivity at the bottom of the pyramid: the use of artificial neural networks (ANNs)." International Journal of Operations & Production Management 42, no. 4 (March 8, 2022): 552–76. http://dx.doi.org/10.1108/ijopm-01-2021-0031.

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PurposeIn this research, the authors apply artificial neural networks (ANNs) to uncover non-linear relationships among factors that influence the productivity of ragpickers in the Indian context.Design/methodology/approachA broad long-term action research program provides a means to shape the research question and posit relevant factors, whereas ANNs capture the true underlying non-linear relationships. ANN models the relationships between four independent variables and three forms of waste value chains without assuming any distributional forms. The authors apply bootstrapping in conjunction with ANNs.FindingsThe authors identify four elements that influence ragpickers’ productivity: receptiveness to non-governmental organizations, literacy, the deployment of proper equipment/technology and group size.Research limitations/implicationsThis study provides a unique way to analyze bottom of the pyramid (BoP) operations via ANNs.Social implicationsThis study provides a road map to help ragpickers in India raise incomes while simultaneously improving recycling rates.Originality/valueThis research is grounded in the stakeholder resource-based view and the network–individual–resource model. It generalizes these theories to the informal waste value chain at BoP communities.

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Ahmad, Wan Muhamad Amir W., Faraz Ahmed, Nor Farid Mohd Noor, Nor Azlida Aleng, Farah Muna Mohamad Ghazali, and Mohammad Khursheed Alam. "Prediction and Elucidation of Triglycerides Levels Using a Machine Learning and Linear Fuzzy Modelling Approach." BioMed Research International 2022 (February 24, 2022): 1–7. http://dx.doi.org/10.1155/2022/7511806.

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Introduction. Triglycerides are lipids composed of fatty acids that provide energy to the cell. These compounds are delivered to the body’s cells via lipoproteins found in the bloodstream. Increased blood triglyceride levels have been associated with high-fat or high-carbohydrate diets. Generally, increased triglyceride levels occur in conjunction with other symptoms that are difficult to notice and recognize. Objectives. The study’s goal was to develop and predict the model that could be used to explain the relationship between triglycerides and waist circumference, high-density lipoprotein (HDL), and hypertension status by determining the relationship between triglycerides and waist circumference, HDL, and hypertension status. This model was developed using qualitative predictor variables and incorporated data bootstrapping multilayer perceptron neural networks and fuzzy linear regression. Materials and procedures. This was a public health study that combined retrospective data analysis with methodology development. The medical records of patients who attended outpatient clinics at Hospital Universiti Sains Malaysia (USM) were collected and analyzed. This was to provide a more extensive illustration of the methods developed. Screening and selection of patient data were necessary following the inclusion and exclusion criteria. The patient’s medical record was used to obtain triglycerides, high-density lipoprotein (HDL), waist circumference, and hypertension status. Due to the critical nature of the variable, it was chosen to aid the clinical expert. The R-Studio software was used to develop the associated syntax for the hybrid model, which would define the association between the examined variables. The purpose of this study is to create a technique for the clinical trial design that utilizes bootstrapping, Qualitative Predictor Variables (QPV), Multiple Linear Regression (MLR), Artificial Neural Networks (ANNs), and Fuzzy Regression (FR). All analyses were performed using the newly introduced R syntax. The research developed a fuzzy linear model that increased modelling performance by incorporating clinically significant factors and validated variables via Multilayer Perceptron (MLP). Conclusion. The proposed technique for modelling and prediction appeared to be the ideal combination of bootstrap, Multilayer Feed Forward (MLFF) neural network, and fuzzy linear regression. The created syntax is currently being evaluated and validated clinically. For modelling and prediction, the proposed technique looked to be the best, as it incorporated bootstrap, MLFF neural network, and fuzzy linear regression. The established syntax is now being utilized in the clinic to evaluate and validate the outcome. In terms of variable selection, modelling, and model validation, this strategy was superior to earlier approaches for fuzzy regression modelling.

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Ovcharenko, Evgeny, Anton Kutikhin, Olga Gruzdeva, Anastasia Kuzmina, Tamara Slesareva, Elena Brusina, Svetlana Kudasheva, et al. "Cardiovascular and Renal Comorbidities Included into Neural Networks Predict the Outcome in COVID-19 Patients Admitted to an Intensive Care Unit: Three-Center, Cross-Validation, Age- and Sex-Matched Study." Journal of Cardiovascular Development and Disease 10, no. 2 (January 23, 2023): 39. http://dx.doi.org/10.3390/jcdd10020039.

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Here, we performed a multicenter, age- and sex-matched study to compare the efficiency of various machine learning algorithms in the prediction of COVID-19 fatal outcomes and to develop sensitive, specific, and robust artificial intelligence tools for the prompt triage of patients with severe COVID-19 in the intensive care unit setting. In a challenge against other established machine learning algorithms (decision trees, random forests, extra trees, neural networks, k-nearest neighbors, and gradient boosting: XGBoost, LightGBM, and CatBoost) and multivariate logistic regression as a reference, neural networks demonstrated the highest sensitivity, sufficient specificity, and excellent robustness. Further, neural networks based on coronary artery disease/chronic heart failure, stage 3-5 chronic kidney disease, blood urea nitrogen, and C-reactive protein as the predictors exceeded 90% sensitivity and 80% specificity, reaching AUROC of 0.866 at primary cross-validation and 0.849 at secondary cross-validation on virtual samples generated by the bootstrapping procedure. These results underscore the impact of cardiovascular and renal comorbidities in the context of thrombotic complications characteristic of severe COVID-19. As aforementioned predictors can be obtained from the case histories or are inexpensive to be measured at admission to the intensive care unit, we suggest this predictor composition is useful for the triage of critically ill COVID-19 patients.

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Bischl, B., O. Mersmann, H. Trautmann, and C. Weihs. "Resampling Methods for Meta-Model Validation with Recommendations for Evolutionary Computation." Evolutionary Computation 20, no. 2 (June 2012): 249–75. http://dx.doi.org/10.1162/evco_a_00069.

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Meta-modeling has become a crucial tool in solving expensive optimization problems. Much of the work in the past has focused on finding a good regression method to model the fitness function. Examples include classical linear regression, splines, neural networks, Kriging and support vector regression. This paper specifically draws attention to the fact that assessing model accuracy is a crucial aspect in the meta-modeling framework. Resampling strategies such as cross-validation, subsampling, bootstrapping, and nested resampling are prominent methods for model validation and are systematically discussed with respect to possible pitfalls, shortcomings, and specific features. A survey of meta-modeling techniques within evolutionary optimization is provided. In addition, practical examples illustrating some of the pitfalls associated with model selection and performance assessment are presented. Finally, recommendations are given for choosing a model validation technique for a particular setting.

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Jayachandran, A., and B. AnuSheeba. "Hybrid Melanoma Classification System Using Multi-Layer Fuzzy C-Means Clustering and Deep Convolutional Neural Network." Journal of Medical Imaging and Health Informatics 11, no. 11 (November 1, 2021): 2709–15. http://dx.doi.org/10.1166/jmihi.2021.3873.

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Skin cancer is considered one of the most common type of cancer in several countries. Due to the difficulty and subjectivity in the clinical diagnosis of skin lesions, Computer-Aided Diagnosis systems are being developed for assist experts to perform more reliable diagnosis. The clinical analysis and diagnosis of skin lesions relies not only on the visual information but also on the context information provided by the patient. Skin lesion segmentation plays a significant part in the earlier and precise identification of skin cancer using computer aided diagnosis (CAD) models. But, the segmentation of skin lesions in dermoscopic images is a difficult process due to the constraints of artefacts (hairs, gel bubbles, ruler markers), unclear boundaries, poor and so on. In this work, multi class skin lesion classification system is developed based on multi layered Fuzzy C-means clustering and deep convolutional neural networks. Evaluate the performance of the proposed MLFCM with DCNN model on multi class skin cancer Dermoscopy images. Our results suggest that it is possible to boost the performance of skin lesion segmentation and classification simultaneously via training a unified model to perform both tasks in a mutual bootstrapping way.

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Wanke, Peter, Carlos Barros, and Md Abul Kalam Azad. "Measuring efficiency drivers and productive slacks in UK auditing firms." Benchmarking: An International Journal 24, no. 3 (April 3, 2017): 806–23. http://dx.doi.org/10.1108/bij-07-2016-0102.

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Purpose The purpose of this paper is to measure the efficiency and the productive slacks of a sample of major UK auditing firms between 2005 and 2012. Design/methodology/approach Using the bootstrapping technique as the cornerstone method, DEA estimates were computed, allowing the test for differences in the levels of efficiency and in the potentials for decreasing inputs and increasing outputs. Then, neural networks were combined with DEA estimates to model, with effective predictive ability, the drivers of auditing firms’ performance. Findings The findings indicate an ambiguous impact of regulatory policies on efficiency levels. Results also indicate an eventual capacity shortfall, since the most important productive resource does not seem to cope with demand growth in the future. Originality/value This paper aims to focus on efficiency, complementing previous research in the UK that focuses on productivity, and helps in establishing a decision-support system to slack evaluation.

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Ascencio-Cabral, Azucena, and Constantino Carlos Reyes-Aldasoro. "Comparison of Convolutional Neural Networks and Transformers for the Classification of Images of COVID-19, Pneumonia and Healthy Individuals as Observed with Computed Tomography." Journal of Imaging 8, no. 9 (September 1, 2022): 237. http://dx.doi.org/10.3390/jimaging8090237.

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In this work, the performance of five deep learning architectures in classifying COVID-19 in a multi-class set-up is evaluated. The classifiers were built on pretrained ResNet-50, ResNet-50r (with kernel size 5×5 in the first convolutional layer), DenseNet-121, MobileNet-v3 and the state-of-the-art CaiT-24-XXS-224 (CaiT) transformer. The cross entropy and weighted cross entropy were minimised with Adam and AdamW. In total, 20 experiments were conducted with 10 repetitions and obtained the following metrics: accuracy (Acc), balanced accuracy (BA), F1 and F2 from the general Fβ macro score, Matthew’s Correlation Coefficient (MCC), sensitivity (Sens) and specificity (Spec) followed by bootstrapping. The performance of the classifiers was compared by using the Friedman–Nemenyi test. The results show that less complex architectures such as ResNet-50, ResNet-50r and DenseNet-121 were able to achieve better generalization with rankings of 1.53, 1.71 and 3.05 for the Matthew Correlation Coefficient, respectively, while MobileNet-v3 and CaiT obtained rankings of 3.72 and 5.0, respectively.

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Sebastian, C., B. Boom, T. van Lankveld, E. Bondarev, and P. H. N. De With. "BOOTSTRAPPED CNNS FOR BUILDING SEGMENTATION ON RGB-D AERIAL IMAGERY." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-4 (September 19, 2018): 187–92. http://dx.doi.org/10.5194/isprs-annals-iv-4-187-2018.

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Abstract. Detection of buildings and other objects from aerial images has various applications in urban planning and map making. Automated building detection from aerial imagery is a challenging task, as it is prone to varying lighting conditions, shadows and occlusions. Convolutional Neural Networks (CNNs) are robust against some of these variations, although they fail to distinguish easy and difficult examples. We train a detection algorithm from RGB-D images to obtain a segmented mask by using the CNN architecture DenseNet. First, we improve the performance of the model by applying a statistical re-sampling technique called Bootstrapping and demonstrate that more informative examples are retained. Second, the proposed method outperforms the non-bootstrapped version by utilizing only one-sixth of the original training data and it obtains a precision-recall break-even of 95.10% on our aerial imagery dataset.

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Kleinert, Felix, Lukas H. Leufen, and Martin G. Schultz. "IntelliO3-ts v1.0: a neural network approach to predict near-surface ozone concentrations in Germany." Geoscientific Model Development 14, no. 1 (January 4, 2021): 1–25. http://dx.doi.org/10.5194/gmd-14-1-2021.

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Abstract. The prediction of near-surface ozone concentrations is important for supporting regulatory procedures for the protection of humans from high exposure to air pollution. In this study, we introduce a data-driven forecasting model named “IntelliO3-ts”, which consists of multiple convolutional neural network (CNN) layers, grouped together as inception blocks. The model is trained with measured multi-year ozone and nitrogen oxide concentrations of more than 300 German measurement stations in rural environments and six meteorological variables from the meteorological COSMO reanalysis. This is by far the most extensive dataset used for time series predictions based on neural networks so far. IntelliO3-ts allows the prediction of daily maximum 8 h average (dma8eu) ozone concentrations for a lead time of up to 4 d, and we show that the model outperforms standard reference models like persistence models. Moreover, we demonstrate that IntelliO3-ts outperforms climatological reference models for the first 2 d, while it does not add any genuine value for longer lead times. We attribute this to the limited deterministic information that is contained in the single-station time series training data. We applied a bootstrapping technique to analyse the influence of different input variables and found that the previous-day ozone concentrations are of major importance, followed by 2 m temperature. As we did not use any geographic information to train IntelliO3-ts in its current version and included no relation between stations, the influence of the horizontal wind components on the model performance is minimal. We expect that the inclusion of advection–diffusion terms in the model could improve results in future versions of our model.

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Bachtiar, Luqman R., Charles P. Unsworth, Richard D. Newcomb, and Edmund J. Crampin. "Multilayer Perceptron Classification of Unknown Volatile Chemicals from the Firing Rates of Insect Olfactory Sensory Neurons and Its Application to Biosensor Design." Neural Computation 25, no. 1 (January 2013): 259–87. http://dx.doi.org/10.1162/neco_a_00386.

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In this letter, we use the firing rates from an array of olfactory sensory neurons (OSNs) of the fruit fly, Drosophila melanogaster, to train an artificial neural network (ANN) to distinguish different chemical classes of volatile odorants. Bootstrapping is implemented for the optimized networks, providing an accurate estimate of a network's predicted values. Initially a simple linear predictor was used to assess the complexity of the data and was found to provide low prediction performance. A nonlinear ANN in the form of a single multilayer perceptron (MLP) was also used, providing a significant increase in prediction performance. The effect of the number of hidden layers and hidden neurons of the MLP was investigated and found to be effective in enhancing network performance with both a single and a double hidden layer investigated separately. A hybrid array of MLPs was investigated and compared against the single MLP architecture. The hybrid MLPs were found to classify all vectors of the validation set, presenting the highest degree of prediction accuracy. Adjustment of the number of hidden neurons was investigated, providing further performance gain. In addition, noise injection was investigated, proving successful for certain network designs. It was found that the best-performing MLP was that of the double-hidden-layer hybrid MLP network without the use of noise injection. Furthermore, the level of performance was examined when different numbers of OSNs used were varied from the maximum of 24 to only 5 OSNs. Finally, the ideal OSNs were identified that optimized network performance. The results obtained from this study provide strong evidence of the usefulness of ANNs in the field of olfaction for the future realization of a signal processing back end for an artificial olfactory biosensor.

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Oehler, F., J. C. Rutherford, and G. Coco. "The use of machine learning algorithms to design a generalized simplified denitrification model." Biogeosciences 7, no. 10 (October 27, 2010): 3311–32. http://dx.doi.org/10.5194/bg-7-3311-2010.

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Abstract. We propose to use machine learning (ML) algorithms to design a simplified denitrification model. Boosted regression trees (BRT) and artificial neural networks (ANN) were used to analyse the relationships and the relative influences of different input variables towards total denitrification, and an ANN was designed as a simplified model to simulate total nitrogen emissions from the denitrification process. To calibrate the BRT and ANN models and test this method, we used a database obtained collating datasets from the literature. We used bootstrapping to compute confidence intervals for the calibration and validation process. Both ML algorithms clearly outperformed a commonly used simplified model of nitrogen emissions, NEMIS, which is based on denitrification potential, temperature, soil water content and nitrate concentration. The ML models used soil organic matter % in place of a denitrification potential and pH as a fifth input variable. The BRT analysis reaffirms the importance of temperature, soil water content and nitrate concentration. Generalization, although limited to the data space of the database used to build the ML models, could be improved if pH is used to differentiate between soil types. Further improvements in model performance and generalization could be achieved by adding more data.

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CAILLAULT, EMILIE, and CHRISTIAN VIARD-GAUDIN. "MIXED DISCRIMINANT TRAINING OF HYBRID ANN/HMM SYSTEMS FOR ONLINE HANDWRITTEN WORD RECOGNITION." International Journal of Pattern Recognition and Artificial Intelligence 21, no. 01 (February 2007): 117–34. http://dx.doi.org/10.1142/s0218001407005338.

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Online handwritten word recognition systems usually rely on Hidden Markov Models (HMMs), which are effective under many circumstances, but suffer some major limitations in real world applications. Artificial neural networks (ANN) appear to be a promising alternative, however they failed to model sequence data such as online handwriting due to their variable lengths. As a consequence, by combining HMMs and ANN, we can expect to take advantage of the robustness and flexibility of the HMMs generative models and of the discriminative power of the ANN. Training such a hybrid system is not straightforward, this is why so few attempts are encountered in literature. We compare several different training schemes: maximum likelihood (ML) and maximum mutual information (MMI) criteria in the framework of online handwriting recognition with a global optimization approach defined at the word level. A new generic criterion mixing generative model and discriminant trainings is proposed, it allows to train a multistate TDNN-HMM system directly at the word level. This architecture is based on an analytical approach with an implicit segmentation. To control the implicit segmentation and to initialize correctly the system without bootstrapping with another recognition system, we have defined a process that constraints the segmentation path and a measure called Average Segmentation Rate (ASR). Recognition experiments on the online IRONOFF database demonstrated the interest of the generic training criterion and the control of the implicit segmentation.

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Baghdadi, Nadiah A., Shatha Khalid Alsayed, Ghalia Amer Malki, Hossam Magdy Balaha, and Sally Mohammed Farghaly Abdelaliem. "An Analysis of Burnout among Female Nurse Educators in Saudi Arabia Using K-Means Clustering." European Journal of Investigation in Health, Psychology and Education 13, no. 1 (December 30, 2022): 33–53. http://dx.doi.org/10.3390/ejihpe13010003.

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Nurse educators are often burnt out and suffer from depression due to their demanding job settings. Biochemical markers of burnout can provide insights into the physiological changes that lead to burnout and may help us prevent burnout symptoms. Research was conducted using a descriptive cross-sectional survey design and a multi-stage sampling method. The ministry of education website provides a list of Saudi Arabian nursing education programs that offer bachelor of science in nursing programs (BSN). The study consisted of 299 qualified participants. Malsach Burnout Inventory (MBI) was used to measure burnout as the dependent variable. The MBI is a 22-item scale that measures depersonalization, accomplishment, and emotional exhaustion during work. Bootstrapping with 5000 replicas was used to address potential non-normality. During this framework, four deep neural networks are created. They all have the same number of layers but differ in the number of neurons they have in the hidden layers. The number of female nurse educators experiencing burnout is moderate (mean = 1.92 ± 0.63). Burnout is also moderately observed in terms of emotional exhaustion (mean = 2.13 ± 0.63), depersonalization (mean = 2.12 ± 0.50), and personal achievement scores (mean = 12 2.38 ± 1.13). It has been shown that stacking the clusters at the end of a column increases their accuracy, which can be considered an important feature when classifying.

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van der Ploeg, Tjeerd, and Robbert Gobbens. "A Comparison of Different Modeling Techniques in Predicting Mortality With the Tilburg Frailty Indicator: Longitudinal Study." JMIR Medical Informatics 10, no. 3 (March 30, 2022): e31480. http://dx.doi.org/10.2196/31480.

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Background Modern modeling techniques may potentially provide more accurate predictions of dichotomous outcomes than classical techniques. Objective In this study, we aimed to examine the predictive performance of eight modeling techniques to predict mortality by frailty. Methods We performed a longitudinal study with a 7-year follow-up. The sample consisted of 479 Dutch community-dwelling people, aged 75 years and older. Frailty was assessed with the Tilburg Frailty Indicator (TFI), a self-report questionnaire. This questionnaire consists of eight physical, four psychological, and three social frailty components. The municipality of Roosendaal, a city in the Netherlands, provided the mortality dates. We compared modeling techniques, such as support vector machine (SVM), neural network (NN), random forest, and least absolute shrinkage and selection operator, as well as classical techniques, such as logistic regression, two Bayesian networks, and recursive partitioning (RP). The area under the receiver operating characteristic curve (AUROC) indicated the performance of the models. The models were validated using bootstrapping. Results We found that the NN model had the best validated performance (AUROC=0.812), followed by the SVM model (AUROC=0.705). The other models had validated AUROC values below 0.700. The RP model had the lowest validated AUROC (0.605). The NN model had the highest optimism (0.156). The predictor variable “difficulty in walking” was important for all models. Conclusions Because of the high optimism of the NN model, we prefer the SVM model for predicting mortality among community-dwelling older people using the TFI, with the addition of “gender” and “age” variables. External validation is a necessary step before applying the prediction models in a new setting.

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Abbasi, Reyhaneh, Peter Balazs, Maria Adelaide Marconi, Doris Nicolakis, Sarah M. Zala, and Dustin J. Penn. "Capturing the songs of mice with an improved detection and classification method for ultrasonic vocalizations (BootSnap)." PLOS Computational Biology 18, no. 5 (May 12, 2022): e1010049. http://dx.doi.org/10.1371/journal.pcbi.1010049.

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House mice communicate through ultrasonic vocalizations (USVs), which are above the range of human hearing (>20 kHz), and several automated methods have been developed for USV detection and classification. Here we evaluate their advantages and disadvantages in a full, systematic comparison, while also presenting a new approach. This study aims to 1) determine the most efficient USV detection tool among the existing methods, and 2) develop a classification model that is more generalizable than existing methods. In both cases, we aim to minimize the user intervention required for processing new data. We compared the performance of four detection methods in an out-of-the-box approach, pretrained DeepSqueak detector, MUPET, USVSEG, and the Automatic Mouse Ultrasound Detector (A-MUD). We also compared these methods to human visual or ‘manual’ classification (ground truth) after assessing its reliability. A-MUD and USVSEG outperformed the other methods in terms of true positive rates using default and adjusted settings, respectively, and A-MUD outperformed USVSEG when false detection rates were also considered. For automating the classification of USVs, we developed BootSnap for supervised classification, which combines bootstrapping on Gammatone Spectrograms and Convolutional Neural Networks algorithms with Snapshot ensemble learning. It successfully classified calls into 12 types, including a new class of false positives that is useful for detection refinement. BootSnap outperformed the pretrained and retrained state-of-the-art tool, and thus it is more generalizable. BootSnap is freely available for scientific use.

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Tsujitani, Masaaki, Katsuhiro Iba, and Yusuke Tanaka. "Neural Discriminant Models, Bootstrapping, and Simulation." ISRN Artificial Intelligence 2012 (March 13, 2012): 1–12. http://dx.doi.org/10.5402/2012/820364.

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This paper considers the feed-forward neural network models for data of mutually exclusive groups and a set of predictor variables. We take into account the bootstrapping based on information criterion when selecting the optimum number of hidden units for a neural network model and the deviance in order to summarize the measure of goodness-of-fit on fitted neural network models. The bootstrapping is also adapted in order to provide estimates of the bias of the excess error in a prediction rule constructed with training samples. Simulated data from known (true) models are analyzed in order to interpret the results using the neural network. In addition, the thyroid disease database, which compares estimated measures of predictive performance, is examined in both a pure training sample study and in a test sample study, in which the realized test sample apparent error rates associated with a constructed prediction rule are reported. Apartment house data of the metropolitan area station with four-class classification are also analyzed in order to assess the bootstrapping by comparing leaving-one-out cross-validation (CV).

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Talib, Rand, Nassif Nabil, and Wonchang Choi. "Optimization-Based Data-Enabled Modeling Technique for HVAC Systems Components." Buildings 10, no. 9 (September 13, 2020): 163. http://dx.doi.org/10.3390/buildings10090163.

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Most of the energy consumed by the residential and commercial buildings in the U.S. is dedicated to space cooling and heating systems, according to the U.S. Energy Information Administration. Therefore, the need for better operation mechanisms of those existing systems become more crucial. The most vital factor for that is the need for accurate models that can accurately predict the system component performance. Therefore, this paper’s primary goal is to develop a new accurate data-driven modeling and optimization technique that can accurately predict the performance of the selected system components. Several data-enabled modeling techniques such as artificial neural networks (ANN), support vector machine (SVM), and aggregated bootstrapping (BSA) are investigated, and model improvements through model structure optimization proposed. The optimization algorithm will determine the optimal model structures and automate the process of the parametric study. The optimization problem is solved using a genetic algorithm (GA) to reduce the error between the simulated and actual data for the testing period. The models predicted the performance of the chilled water variable air volume (VAV) system’s main components of cooling coil and fan power as a function of multiple inputs. Additionally, the packaged DX system compressor modeled, and the compressor power was predicted. The testing results held a low coefficient of variation (CV%) values of 1.22% for the cooling coil, and for the fan model, it was found to be 9.04%. The testing results showed that the proposed modeling and optimization technique could accurately predict the system components’ performance.

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Mehltretter, Joseph, Robert Fratila, David Benrimoh, Adam Kapelner, Kelly Perlman, Emily Snook, Sonia Israel, Caitrin Armstrong, Marc Miresco, and Gustavo Turecki. "Differential Treatment Benefit Prediction for Treatment Selection in Depression: A Deep Learning Analysis of STAR*D and CO-MED Data." Computational Psychiatry 4 (December 2020): 61–75. http://dx.doi.org/10.1162/cpsy_a_00029.

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Depression affects one in nine people, but treatment response rates remain low. There is significant potential in the use of computational modeling techniques to predict individual patient responses and thus provide more personalized treatment. Deep learning is a promising computational technique that can be used for differential treatment selection based on predicted remission probability. Using Sequenced Treatment Alternatives to Relieve Depression (STAR*D) and Combining Medications to Enhance Depression Outcomes (CO-MED) trial data, we employed deep neural networks to predict remission after feature selection. Treatments included were citalopram, escitalopram, bupropion SR plus escitalopram, and venlafaxine plus mirtazapine. Differential treatment benefit was estimated in terms of improvement of population remission rates after application of the model for treatment selection using two approaches: (1) using predictions generated directly from the model (the predicted improvement approach) and (2) using bootstrapping for sample generation and then estimating population remission rate for patients who actually received the drug predicted by the model compared to the general population (the actual improvement approach). Our deep learning model predicted remission in a pooled CO-MED/STAR*D dataset (including four treatments) with an area under the curve of 0.69 using 17 input features. Our actual improvement analysis showed a statistically significant 2.48% absolute improvement (corresponding to a 7.2% relative improvement) in population remission rate ( p = 0.01, CI 2.48% ± 0.5%). Our model serves as proof-of-concept that deep learning approaches, with further refinement and work to address concerns about differences between studies when multiple datasets are used for training, may have utility in differential prediction of antidepressant response when selecting from a number of treatment options.

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Caza, Gregory A., and Alistair Knott. "Pragmatic Bootstrapping: A Neural Network Model of Vocabulary Acquisition." Language Learning and Development 8, no. 2 (April 2012): 113–35. http://dx.doi.org/10.1080/15475441.2011.581144.

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Yan, Lingyong, Xianpei Han, Ben He, and Le Sun. "End-to-End Bootstrapping Neural Network for Entity Set Expansion." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (April 3, 2020): 9402–9. http://dx.doi.org/10.1609/aaai.v34i05.6482.

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Bootstrapping for entity set expansion (ESE) has long been modeled as a multi-step pipelined process. Such a paradigm, unfortunately, often suffers from two main challenges: 1) the entities are expanded in multiple separate steps, which tends to introduce noisy entities and results in the semantic drift problem; 2) it is hard to exploit the high-order entity-pattern relations for entity set expansion. In this paper, we propose an end-to-end bootstrapping neural network for entity set expansion, named BootstrapNet, which models the bootstrapping in an encoder-decoder architecture. In the encoding stage, a graph attention network is used to capture both the first- and the high-order relations between entities and patterns, and encode useful information into their representations. In the decoding stage, the entities are sequentially expanded through a recurrent neural network, which outputs entities at each stage, and its hidden state vectors, representing the target category, are updated at each expansion step. Experimental results demonstrate substantial improvement of our model over previous ESE approaches.

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Wu, Quanwang, Qingsheng Zhu, and Peng Li. "A neural network based reputation bootstrapping approach for service selection." Enterprise Information Systems 9, no. 7 (October 15, 2013): 768–84. http://dx.doi.org/10.1080/17517575.2013.845913.

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Rettenberger, Luca, Marcel Schilling, and Markus Reischl. "Annotation Efforts in Image Segmentation can be Reduced by Neural Network Bootstrapping." Current Directions in Biomedical Engineering 8, no. 2 (August 1, 2022): 329–32. http://dx.doi.org/10.1515/cdbme-2022-1084.

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Abstract Modern medical technology offers potential for the automatic generation of datasets that can be fed into deep learning systems. However, even though raw data for supporting diagnostics can be obtained with manageable effort, generating annotations is burdensome and time-consuming. Since annotating images for semantic segmentation is particularly exhausting, methods to reduce the human effort are especially valuable. We propose a combined framework that utilizes unsupervised machine learning to automatically generate segmentation masks. Experiments on two biomedical datasets show that our approach generates noticeably better annotations than Otsu thresholding and k-means clustering without needing any additional manual effort. Using our framework, unannotated datasets can be amended with pre-annotations fully unsupervised thus reducing the human effort to a minimum.

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Soundiran, Revathi, T. K. Radhakrishnan, and Sivakumaran Natarajan. "Modeling of greenhouse agro-ecosystem using optimally designed bootstrapping artificial neural network." Neural Computing and Applications 31, no. 11 (July 7, 2018): 7821–36. http://dx.doi.org/10.1007/s00521-018-3598-7.

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Ferber, Patrick, Florian Geißer, Felipe Trevizan, Malte Helmert, and Jörg Hoffmann. "Neural Network Heuristic Functions for Classical Planning: Bootstrapping and Comparison to Other Methods." Proceedings of the International Conference on Automated Planning and Scheduling 32 (June 13, 2022): 583–87. http://dx.doi.org/10.1609/icaps.v32i1.19845.

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How can we train neural network (NN) heuristic functions for classical planning, using only states as the NN input? Prior work addressed this question by (a) per-instance imitation learning and/or (b) per-domain learning. The former limits the approach to instances small enough for training data generation, the latter to domains where the necessary knowledge generalizes across instances. Here we explore three methods for (a) that make training data generation scalable through bootstrapping and approximate value iteration. In particular, we introduce a new bootstrapping variant that estimates search effort instead of goal distance, which as we show converges to the perfect heuristic under idealized circumstances. We empirically compare these methods to (a) and (b), aligning three different NN heuristic function learning architectures for cross-comparison in an experiment of unprecedented breadth in this context. Key lessons are that our methods and imitation learning are highly complementary; that per-instance learning often yields stronger heuristics than per-domain learning; and the LAMA planner is still dominant but our methods outperform it in one benchmark domain.

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Wang, Yan, Liqin Tian, and Zhenguo Chen. "A Reputation Bootstrapping Model for E-Commerce Based on Fuzzy DEMATEL Method and Neural Network." IEEE Access 7 (2019): 52266–76. http://dx.doi.org/10.1109/access.2019.2912191.

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Uma Maheswari, A., and N. Revathy. "Comprehensive Review on Effectual Information Retrieval of Semantic Drift using Deep Neural Network." Asian Journal of Computer Science and Technology 8, no. 1 (February 5, 2019): 32–35. http://dx.doi.org/10.51983/ajcst-2019.8.1.2122.

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Semantic drift is a common problem in iterative information extraction. Unsupervised bagging and incorporated distributional similarity is used to reduce the difficulty of semantic drift in iterative bootstrapping algorithms, particularly when extracting large semantic lexicons. In this research work, a method to minimize semantic drift by identifying the (Drifting Points) DPs and removing the effect introduced by the DPs is proposed. Previous methods for identifying drifting errors can be roughly divided into two categories: (1) multi-class based, and (2) single-class based, according to the settings of Information Extraction systems that adopt them. Compared to previous approaches which usually incur substantial loss in recall, DP-based cleaning method can effectively clean a large proportion of semantic drift errors while keeping a high recall.

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Desiani, Anita, Azhar Kholiq Affandi, Shania Putri Andhini, Sugandi Yahdin, Yuli Andirani, and Muhammad Arhami. "Implementation of Sample Sample Bootstrapping for Resampling Pap Smear Single Cell Dataset." Lontar Komputer : Jurnal Ilmiah Teknologi Informasi 13, no. 2 (August 10, 2022): 72. http://dx.doi.org/10.24843/lkjiti.2022.v13.i02.p01.

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The purpose of this study was to determine how the effect of using Bootstrapping Samples for resampling the Harlev dataset in improving the performance of single-cell pap smear classification by dealing with the data imbalance problem. The Harlev dataset used in this study consists of 917 data with 20 attributes. The number of classes on the label had data imbalance in the dataset that affected single-cell pap smear classification performance. The data imbalance in the classification causes machine learning algorithms to produce poor performance in the minority class because they were overwhelmed by the majority class. To overcome it, The resampling data could be used with Sample Bootstrapping. The results of the Sample Bootstrapping were evaluated using the Artificial Neural Network and K-Nearest Neighbors classification methods. The classification used was seven classes and two classes. The classification results using these two methods showed an increase in accuracy, precision, and recall values. The performance improvement reached 10.82% for the two classes classification and 35% for the seven classes classification. It was concluded that Sample Boostrapping was good and robust in improving the classification method.

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Journal articles on the topic 'Bootstrapping neural networks'

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