Multi-label classification by exploiting local positive and negative pairwise label correlation

doi:10.1016/j.neucom.2016.12.073

	Multi-label classification by exploiting local positive and negative pairwise label correlation
	Huang, Jun 1,2; Li, Guorong 1; Wang, Shuhui 3; Xue, Zhe 1; Huang, Qingming 1,3
	2017-09-27
发表期刊	NEUROCOMPUTING
ISSN	0925-2312
卷号	257 页码:164-174
摘要	In multi-label learning, each example is represented by a single instance and associated with multiple class labels. Existing multi-label learning algorithms mainly exploit label correlations globally, by assuming that the label correlations are shared by all the examples. Moreover, these multi-label learning algorithms exploit the positive label correlations among different class labels. In practical applications, however, different examples may share different label correlations, and the labels are not only positive correlated, but also mutually exclusive with each other. In this paper, we propose a simple and effective Bayesian model for multi-label classification by exploiting Local positive and negative Pairwise Label Correlations, named LPLC. In the training stage, the positive and negative label correlations of each ground truth label for all the training examples are discovered. In the test stage, the k nearest neighbors and their corresponding positive and negative pairwise label correlations for each test example are first identified, then we make prediction through maximizing the posterior probability, which is estimated on the label distribution, the local positive and negative pairwise label correlations embodied in the k nearest neighbors. A comparative study with the state-of-the-art approaches manifests a competitive performance of our proposed method. (C) 2017 Elsevier B.V. All rights reserved.
关键词	Multi-label classification k nearest neighbors Local label correlation Positive and negative label correlation
DOI	10.1016/j.neucom.2016.12.073
收录类别	SCI
语种	英语
资助项目	National Basic Research Program of China (973 Program)[2015CB351802] ; National Natural Science Foundation of China[61303153] ; National Natural Science Foundation of China[61332016] ; National Natural Science Foundation of China[61620106009] ; National Natural Science Foundation of China[61572488] ; National Natural Science Foundation of China[61672497] ; 863 program of China[2014AA015202] ; Bureau of Frontier Sciences and Education (CAS)[QYZDJ-SSW-SYS013]
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:000404319800018
出版者	ELSEVIER SCIENCE BV
引用统计	被引频次：78[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/7097
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Li, Guorong; Huang, Qingming
作者单位	1.Univ Chinese Acad Sci, Sch Comp & Control Engn, Beijing 101408, Peoples R China 2.Anhui Univ Technol, Sch Comp Sci & Technol, Maanshan 243032, Peoples R China 3.Chinese Acad Sci, Key Lab Intelligent Informat Proc, Inst Comp Technol, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Huang, Jun,Li, Guorong,Wang, Shuhui,et al. Multi-label classification by exploiting local positive and negative pairwise label correlation[J]. NEUROCOMPUTING,2017,257:164-174.
APA	Huang, Jun,Li, Guorong,Wang, Shuhui,Xue, Zhe,&Huang, Qingming.(2017).Multi-label classification by exploiting local positive and negative pairwise label correlation.NEUROCOMPUTING,257,164-174.
MLA	Huang, Jun,et al."Multi-label classification by exploiting local positive and negative pairwise label correlation".NEUROCOMPUTING 257(2017):164-174.