文献类型：期刊文献

英文题名：Cost-sensitive Fuzzy Multiple Kernel Learning for imbalanced problem

作者：Wang, Zhe[1];Wang, Bolu[1];Cheng, Yang[1];Li, Dongdong[1];Zhang, Jing[1]

机构：[1]East China Univ Sci & Technol, Dept Comp Sci & Engn, Shanghai 200237, Peoples R China

年份：2019

卷号：366

起止页码：178

外文期刊名：NEUROCOMPUTING

收录：;EI(收录号：20193207289479);WOS:【SCI-EXPANDED(收录号:WOS:000488202500018)】；

基金：This work is supported by Natural Science Foundation of China under Grant No. 61672227, "Shuguang Program" supported by Shanghai Education Development Foundation and Shanghai Municipal Education Commission, Natural Science Foundation of China under Grant No. 61806078, National Key R&D Program of China under Grant No. 2018YFC0910500, and Shanghai Informatization Development Special Fund Project for Big Data Development under Grant No. 201901043.

语种：英文

外文关键词：Imbalanced data; Multiple Kernel Learning; Fuzzy membership; Information entropy; Cost-sensitive learning

摘要：Multiple Kernel Learning (MKL) improves the classification accuracy by exploring different formulations of data. However, its classification performance is still unsatisfactory in imbalanced problems. To address this issue, we take the characteristics of imbalanced data into account by introducing the fuzzy memberships. In our work, the fuzzy memberships are determined by both the entropies of samples and the cost for each class so as to make different samples have different contributions to the decision boundary. Thus the newly proposed method can result in more favorable classification performances on imbalanced datasets. Further, we introduce the fuzzy memberships into existing MKL to form a new algorithm Cost-sensitive Fuzzy Multiple Kernel Learning named CFMKL in short. Experimental results validate the great effectiveness of the proposed CFMKL on synthetic, real-world binary and multi-class imbalanced datasets. The major contributions of this paper are as follows. Firstly, extending the MKL to handle the imbalanced problems for the first time. Secondly, generating a new fuzzy memberships function by both the entropy and the cost-sensitive. Thirdly, proposing a new algorithm named CFMKL for imbalanced problems and validating its effectiveness. (C) 2019 Elsevier B.V. All rights reserved.