文献类型：期刊文献

英文题名：Joint Ranking SVM and Binary Relevance with robust Low-rank learning for multi-label classification

作者：Wu, Guoqiang[1,2];Zheng, Ruobing[3];Tian, Yingjie[2,4,5];Liu, Dalian[6]

第一作者：Wu, Guoqiang

通讯作者：Tian, YJ[1];Liu, DL[2]

机构：[1]Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing 100049, Peoples R China;[2]Chinese Acad Sci, Res Ctr Fictitious Econ & Data Sci, Beijing 100190, Peoples R China;[3]Chinese Acad Sci, Comp Network Informat Ctr, Beijing 100190, Peoples R China;[4]Univ Chinese Acad Sci, Sch Econ & Management, Beijing 100190, Peoples R China;[5]Chinese Acad Sci, Key Lab Big Data Min & Knowledge Management, Beijing 100190, Peoples R China;[6]Beijing Union Univ, Dept Basic Course Teaching, Beijing 100101, Peoples R China

第一机构：Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing 100049, Peoples R China

通讯机构：[1]corresponding author), Chinese Acad Sci, Res Ctr Fictitious Econ & Data Sci, Beijing 100190, Peoples R China;[2]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：2020

卷号：122

起止页码：24-39

外文期刊名：NEURAL NETWORKS

收录：;EI(收录号：20194407609673);Scopus(收录号：2-s2.0-85074129321);WOS:【SCI-EXPANDED(收录号:WOS:000505021700002)】；

基金：This work has been partially supported by grants from: Science and Technology Service Network Program of Chinese Academy of Sciences (STS Program, No. KFJ-STS-ZDTP-060), and National Natural Science Foundation of China (No. 71731009, 61472390, 71331005, 91546201).

语种：英文

外文关键词：Multi-label classification; Rank-SVM; Binary Relevance; Robust Low-rank learning; Kernel methods

摘要：Multi-label classification studies the task where each example belongs to multiple labels simultaneously. As a representative method, Ranking Support Vector Machine (Rank-SVM) aims to minimize the Ranking Loss and can also mitigate the negative influence of the class-imbalance issue. However, due to its stacking-style way for thresholding, it may suffer error accumulation and thus reduces the final classification performance. Binary Relevance (BR) is another typical method, which aims to minimize the Hamming Loss and only needs one-step learning. Nevertheless, it might have the class-imbalance issue and does not take into account label correlations. To address the above issues, we propose a novel multi-label classification model, which joints Ranking support vector machine and Binary Relevance with robust Low-rank learning (RBRL). RBRL inherits the ranking loss minimization advantages of Rank-SVM, and thus overcomes the disadvantages of BR suffering the class-imbalance issue and ignoring the label correlations. Meanwhile, it utilizes the hamming loss minimization and one-step learning advantages of BR, and thus tackles the disadvantages of Rank-SVM including another thresholding learning step. Besides, a low-rank constraint is utilized to further exploit high-order label correlations under the assumption of low dimensional label space. Furthermore, to achieve nonlinear multi-label classifiers, we derive the kernelization RBRL. Two accelerated proximal gradient methods (APG) are used to solve the optimization problems efficiently. Extensive comparative experiments with several state-of-the-art methods illustrate a highly competitive or superior performance of our method RBRL. (c) 2019 Elsevier Ltd. All rights reserved.