文献类型：期刊文献

英文题名：Cost-sensitive multi-label learning with positive and negative label pairwise correlations

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

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 & Control Engn, Beijing 100049, Peoples R China;[2]Chinese Acad Sci, Res Ctr Fictitious Econ & Data Sci, Beijing 100190, Peoples R China;[3]Univ Chinese Acad Sci, Sch Econ & Management, Beijing 100190, Peoples R China;[4]Chinese Acad Sci, Key Lab Big Data Min & Knowledge 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 Comp & Control Engn, 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：2018

卷号：108

起止页码：411-423

外文期刊名：NEURAL NETWORKS

收录：;EI(收录号：20184105931362);Scopus(收录号：2-s2.0-85054462065);WOS:【SCI-EXPANDED(收录号:WOS:000450298900030)】；

基金：This work has been partially supported by grants from National Natural Science Foundation of China (Nos. 61472390, 71731009, 71331005, and 91546201), and the Beijing Natural Science Foundation (No. 1162005) and Premium Funding Project for Academic Human Resources Development in Beijing Union University.

语种：英文

外文关键词：Binary Relevance; Multi-label learning; Cost-sensitive; Positive label correlations; Negative label correlations

摘要：Multi-label learning is the problem where each instance is associated with multiple labels simultaneously. Binary Relevance (BR) is a representative algorithm for multi-label learning. However, it may suffer the class-imbalance issue especially when the label space is large. Besides, it ignores the label correlations, which is of importance to improve the performance. Moreover, labels might have positive and negative correlations in real applications, but existing methods seldom exploit the negative label correlations. In this paper, we propose a novel Cost-sensitive multi-label learning model with Positive and Negative Label pairwise correlations (CPNL), which extends BR to tackle the above issues. The kernel extension of the linear model is also provided to explore complex input-output relationships. Moreover, we adopt two accelerated gradient methods (AGM) to efficiently solve the linear and kernel models. Experimental results show that our approach CPNL achieves a competitive performance to some state-of-the-art approaches for multi-label learning. (C) 2018 Elsevier Ltd. All rights reserved.