文献类型：期刊文献

英文题名：Adaptive FH-SVM for Imbalanced Classification

作者：Wang, Qi[1,2,3];Tian, Yingjie[2,3,4];Liu, Dalian[5]

第一作者：Wang, Qi

通讯作者：Liu, DL[1]

机构：[1]Univ Chinese Acad Sci, Sch Math Sci, Beijing 100049, Peoples R China;[2]Chinese Acad Sci, Res Ctr Fictit Econ & Data Sci, Beijing 100190, Peoples R China;[3]Chinese Acad Sci, Key Lab Big Data Min & Knowledge Management, Beijing 100190, Peoples R China;[4]Univ Chinese Acad Sci, Sch Econ & Management, Beijing 100190, Peoples R China;[5]Beijing Union Univ, Dept Basic Course Teaching, Beijing 100101, Peoples R China

第一机构：Univ Chinese Acad Sci, Sch Math Sci, Beijing 100049, Peoples R China

通讯机构：[1]corresponding author), Beijing Union Univ, Dept Basic Course Teaching, Beijing 100101, Peoples R China.|[1141788]北京联合大学基础教学部;[11417]北京联合大学;

年份：2019

卷号：7

起止页码：130410-130422

外文期刊名：IEEE ACCESS

收录：;EI(收录号：20200308030883);Scopus(收录号：2-s2.0-85077733122);WOS:【SCI-EXPANDED(收录号:WOS:000487543000002)】；

基金：This work was supported in part by the Science and Technology Service Network Program of Chinese Academy of Sciences (STS Program), under Grant KFJ-STS-ZDTP-060, in part by the National Natural Science Foundation of China under Grant 71731009, Grant 61472390, Grant 71331005, and Grant 91546201, and in part by the Premium Funding Project for Academic Human Resources Development in Beijing Union University.

语种：英文

外文关键词：Focal loss; hinge loss; class imbalance; support vector machines (SVMs)

摘要：Support vector machines (SVMs), powerful learning methods, have been popular among machine learning researches due to their strong performance on both classification and regression problems. However, traditional SVM making use of Hinge Loss cannot deal with class imbalance problems, because it applies the same weight of loss to each class. Recently, Focal Loss has been widely used for deep learning to address the imbalanced datasets. The significant effectiveness of Focal loss attracts the attention in many fields, such as object detection, semantic segmentation. Inspired by Focal loss, we reconstructed Hinge Loss with the scaling factor of Focal loss, called FH Loss, which not only deals with the class imbalance problems but also preserve the distinctive property of Hinge loss. Owing to the difficulty of the trade-off between positive and negative accuracy in imbalanced classification, FH loss pays more attention on minority class and misclassified instances to improve the accuracy of each class, further to reduce the influence of imbalance. In addition, due to the difficulty of solving SVM with FH loss, we propose an improved model with modified FH loss, called Adaptive FH-SVM. The algorithm solves the optimization problem iteratively and adaptively updates the FH loss of each instance. Experimental results on 31 binary imbalanced datasets demonstrate the effectiveness of our proposed method.