文献类型：期刊文献

英文题名：Entropy-based hybrid sampling ensemble learning for imbalanced data

作者：Dongdong, Li[1,2,3];Ziqiu, Chi[2];Bolu, Wang[2];Zhe, Wang[1,2];Hai, Yang[1,2];Wenli, Du[1]

机构：[1]East China Univ Sci & Technol, Minist Educ, Key Lab Adv Control & Optimizat Chem Proc, Shanghai 200237, Peoples R China;[2]East China Univ Sci & Technol, Dept Comp Sci & Engn, Shanghai, Peoples R China;[3]Soochow Univ, Prov Key Lab Comp Informat Proc Technol, Suzhou, Peoples R China

年份：2021

卷号：36

期号：7

起止页码：3039

外文期刊名：INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS

收录：;EI(收录号：20211010034076);WOS:【SCI-EXPANDED(收录号:WOS:000625450000001)】；

基金：This study is supported by National Major Scientific and Technological Special Project for "Significant New Drugs Development" under Grant No. 2019ZX09201004, Shanghai Science and Technology Program "Distributed and generative few-shot algorithm and theory research" under Grant No. 20511100600, Natural Science Foundation of China under Grant No. 62076094, Natural Science Foundations of China under Grant No. 61806078, National Key Research and Development Project of Ministry of Science and Technology of China under Grant No. 2018AAA0101302, National Science Foundation of China for Distinguished Young Scholars under Grant No. 61725301.

语种：英文

外文关键词：ensemble; hybrid sampling; imbalanced data; information entropy; pattern recognition

摘要：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.