文献类型：期刊文献

英文题名：Entropy and Confidence-Based Undersampling Boosting Random Forests for Imbalanced Problems

作者：Wang, Zhe[1,2];Cao, Chenjie[2];Zhu, Yujin[2]

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 200237, Peoples R China

年份：2020

卷号：31

期号：12

起止页码：5178

外文期刊名：IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS

收录：;EI(收录号：20200808189053);WOS:【SCI-EXPANDED(收录号:WOS:000595533300012)】；

基金：This work was supported in part by Natural Science Foundation of China under Grant No. 61672227, in part by "Shuguang Program" supported by Shanghai Education Development Foundation and Shanghai Municipal Education Commission, in part by Natural Science Foundations of China under Grant No. 61806078, in part by National Major Scientific and Technological Special Project for "Significant New Drugs Development" under Grant No. 2019ZX09201004, in part by the Special Fund Project for Shanghai Informatization Development in Big Data under Grant No. 201901043, and in part by National Key R&D Program of China under Grant No. 2018YFC0910500.

语种：英文

外文关键词：Entropy; Radio frequency; Boosting; Decision trees; Training; Random forests; Heuristic algorithms; Confidence; ensemble learning; entropy; imbalanced problems; random forests (RFs); undersampling

摘要：In this article, we propose a novel entropy and confidence-based undersampling boosting (ECUBoost) framework to solve imbalanced problems. The boosting-based ensemble is combined with a new undersampling method to improve the generalization performance. To avoid losing informative samples during the data preprocessing of the boosting-based ensemble, both confidence and entropy are used in ECUBoost as benchmarks to ensure the validity and structural distribution of the majority samples during the undersampling. Furthermore, different from other iterative dynamic resampling methods, ECUBoost based on confidence can be applied to algorithms without iterations such as decision trees. Meanwhile, random forests are used as base classifiers in ECUBoost. Furthermore, experimental results on both artificial data sets and KEEL data sets prove the effectiveness of the proposed method.