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A resampling ensemble algorithm for classification of imbalance problems

Authors:

Xiaohu ShiAuthors Info & Claims

Neurocomputing, Volume 143

Pages 57 - 67

https://doi.org/10.1016/j.neucom.2014.06.021

Published: 01 November 2014 Publication History

Abstract

In this paper, a resampling ensemble algorithm is developed focused on the classification problems for imbalanced datasets. In the method, the small classes are oversampled and large classes are undersampled. The resampling scale is determined by the ratio of the min class number and max class number. And multiple machine learning methods are selected to construct the ensemble. Numerical results show that the algorithm performance is highly related to the ratio of minority class number and attribute number. When the ratio is less than 3, the performance will be greatly hindered. Experimental results also show that the ensemble of different types of methods could improve the algorithm performance efficiently.

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Cited By

HR SB A(2024)Exploratory Analysis of Methods, Techniques, and Metrics to Handle Class Imbalance ProblemProcedia Computer Science10.1016/j.procs.2024.04.082235:C(863-877)Online publication date: 24-Jul-2024
https://dl.acm.org/doi/10.1016/j.procs.2024.04.082
Hou BFu CXue M(2024)An extended belief rule-based system with hybrid sampling strategy for imbalanced rule baseInformation Sciences: an International Journal10.1016/j.ins.2024.121288684:COnline publication date: 1-Dec-2024
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Kumar Panda DDas STownley S(2023)Hyperparameter optimized classification pipeline for handling unbalanced urban and rural energy consumption patternsExpert Systems with Applications: An International Journal10.1016/j.eswa.2022.119127214:COnline publication date: 15-Mar-2023
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cover image Neurocomputing

Neurocomputing Volume 143, Issue

November, 2014

362 pages

ISSN:0925-2312

Issue’s Table of Contents

Copyright © Elsevier B.V. © 2014.

Publisher

Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 November 2014

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Cited By

HR SB A(2024)Exploratory Analysis of Methods, Techniques, and Metrics to Handle Class Imbalance ProblemProcedia Computer Science10.1016/j.procs.2024.04.082235:C(863-877)Online publication date: 24-Jul-2024
https://dl.acm.org/doi/10.1016/j.procs.2024.04.082
Hou BFu CXue M(2024)An extended belief rule-based system with hybrid sampling strategy for imbalanced rule baseInformation Sciences: an International Journal10.1016/j.ins.2024.121288684:COnline publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1016/j.ins.2024.121288
Kumar Panda DDas STownley S(2023)Hyperparameter optimized classification pipeline for handling unbalanced urban and rural energy consumption patternsExpert Systems with Applications: An International Journal10.1016/j.eswa.2022.119127214:COnline publication date: 15-Mar-2023
https://dl.acm.org/doi/10.1016/j.eswa.2022.119127
Pan XJia RHuang JWang H(2022)A resistance outlier sampling algorithm for imbalanced data predictionIntelligent Data Analysis10.3233/IDA-21151926:3(583-598)Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.3233/IDA-211519
Zhao JLiu XZhao W(2022)Balanced and Accurate Pseudo-Labels for Semi-Supervised Image ClassificationACM Transactions on Multimedia Computing, Communications, and Applications10.1145/350671118:3s(1-18)Online publication date: 31-Oct-2022
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Xie YQiu MZhang HPeng LChen Z(2022)Gaussian Distribution Based Oversampling for Imbalanced Data ClassificationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2020.298596534:2(667-679)Online publication date: 1-Feb-2022
https://dl.acm.org/doi/10.1109/TKDE.2020.2985965
Hui YMei XJiang GZhao FMa ZTao T(2022)Assembly quality evaluation for linear axis of machine tool using data-driven modeling approachJournal of Intelligent Manufacturing10.1007/s10845-020-01666-y33:3(753-769)Online publication date: 1-Mar-2022
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Wang LQi SLiu YLou HZuo X(2021)Bagging k-dependence Bayesian network classifiersIntelligent Data Analysis10.3233/IDA-20512525:3(641-667)Online publication date: 1-Jan-2021
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Wang ZJia PXu XWang BZhu YLi D(2021)Sample and feature selecting based ensemble learning for imbalanced problemsApplied Soft Computing10.1016/j.asoc.2021.107884113:PAOnline publication date: 1-Dec-2021
https://dl.acm.org/doi/10.1016/j.asoc.2021.107884
Dongdong LZiqiu CBolu WZhe WHai YWenli D(2021)Entropy‐based hybrid sampling ensemble learning for imbalanced dataInternational Journal of Intelligent Systems10.1002/int.2238836:7(3039-3067)Online publication date: 28-May-2021
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