Entropy‐based hybrid sampling ensemble learning for imbalanced data

International Journal of Intelligent SystemsVolume 36, Issue 7 p. 3039-3067 RESEARCH ARTICLE Entropy-based hybrid sampling ensemble learning for imbalanced data Li Dongdong, Li Dongdong orcid.org/0000-0002-1880-8054 Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai, China Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai, China Provincial Key Laboratory for Computer Information Processing Technology, Soochow University, Suzhou, ChinaSearch for more papers by this authorChi Ziqiu, Chi Ziqiu orcid.org/0000-0002-3984-5532 Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai, ChinaSearch for more papers by this authorWang Bolu, Wang Bolu orcid.org/0000-0003-1756-3574 Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai, ChinaSearch for more papers by this authorWang Zhe, Corresponding Author Wang Zhe [email protected] orcid.org/0000-0002-3759-2041 Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai, China Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai, China Correspondence Wang Zhe, Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, 200237 Shanghai, China. Email: [email protected]Search for more papers by this authorYang Hai, Yang Hai orcid.org/0000-0002-1161-4337 Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai, China Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai, ChinaSearch for more papers by this authorDu Wenli, Du Wenli orcid.org/0000-0002-2676-6341 Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai, ChinaSearch for more papers by this author Li Dongdong, Li Dongdong orcid.org/0000-0002-1880-8054 Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai, China Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai, China Provincial Key Laboratory for Computer Information Processing Technology, Soochow University, Suzhou, ChinaSearch for more papers by this authorChi Ziqiu, Chi Ziqiu orcid.org/0000-0002-3984-5532 Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai, ChinaSearch for more papers by this authorWang Bolu, Wang Bolu orcid.org/0000-0003-1756-3574 Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai, ChinaSearch for more papers by this authorWang Zhe, Corresponding Author Wang Zhe [email protected] orcid.org/0000-0002-3759-2041 Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai, China Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai, China Correspondence Wang Zhe, Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, 200237 Shanghai, China. Email: [email protected]Search for more papers by this authorYang Hai, Yang Hai orcid.org/0000-0002-1161-4337 Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai, China Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai, ChinaSearch for more papers by this authorDu Wenli, Du Wenli orcid.org/0000-0002-2676-6341 Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai, ChinaSearch for more papers by this author First published: 05 March 2021 https://doi.org/10.1002/int.22388Citations: 2Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Abstract Sampling method is one of the most commonly used techniques in dealing with imbalanced data. Most of the existing undersampling methods randomly select samples from negative class with replacement. However, it may lose some important information of the training data. Moreover, increasing the positive data by oversampling in high imbalanced situations may cause the overlapping problem. To overcome these problems, this paper proposes a hybrid sampling method. The method takes the distributions of the training data into consideration by the information entropy, thus distinguishing the important samples in the undersampling procedure. Meanwhile, since the positive data only extend to the size of each subset of the negative class in the oversampling, the overlapping problem is relieved. Further, the method retains all the data in the training procedure and generates various data views from the original training data. Then each view is handled with an individual basic classifier. Finally, all the basic classifiers are combined by the ensemble method. The newly proposed method is named as Entropy-based Hybrid Sampling Ensemble Learning (EHSEL). In addition, the EHSEL is applied to three different kinds of basic classifiers to validate its robustness. Experiments results show the great effectiveness of the EHSEL on real-world imbalanced data sets. CONFLICT OF INTERESTS The authors of this manuscript state that there is no conflict of interests between this manuscript and other published works. Citing Literature Volume36, Issue7July 2021Pages 3039-3067 RelatedInformation