文献类型：期刊文献

英文题名：Multi-view learning with fisher kernel and bi-bagging for imbalanced problem

作者：Wang, Zhe[1,2];Zhu, Yiwen[2];Chen, Zhaozhi[2];Zhang, Jing[2];Du, Wenli[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 200237, Peoples R China

年份：2019

卷号：49

期号：8

起止页码：3109

外文期刊名：APPLIED INTELLIGENCE

收录：;EI(收录号：20191106628233);WOS:【SCI-EXPANDED(收录号:WOS:000475519300020)】；

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, and National Science Foundation of China for Distinguished Young Scholars under Grant 61725301.

语种：英文

外文关键词：Fisher kernel; Multi-view learning; Ensemble learning; Imbalanced learning; Pattern recognition

摘要：Existing approaches for handling imbalanced problem are based on the discriminant approaches, while only little attention is dedicated to mining the probability information provided by generative approaches. Moreover, the multi-view learning trains classifier through combining different representations of data for improving the performance of classifier in imbalanced classification. In this paper, a learning framework consisting of fisher kernel and Bi-Bagging is proposed for imbalanced problem. The Fisher kernel is employed to integrate the probability information into the pristine feature of data. Thus, the generated fisher vector contain better discriminatory information. However, the generated fisher vector may lead to high-dimension overfitting. So the dataset represented by the fisher vector is then processed by Bi-Bagging to generate multi-view data and balanced training subsets, which not only reduces the high dimension of generated fisher vector but also promotes the accuracy of minority instances. In one word, the combination of fisher kernel and Bi-Bagging makes use of the probability information in the pristine feature and generates balanced multi-view training subsets with adequate dimension. Therefore, the proposed learning framework is independent of specific models, and the base classifier of the learning framework can be replaced by different linear classifier. Two experimental strategies are implemented to validate the effectiveness of the proposed learning framework for imbalanced datasets on 30 KEEL datasets.