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

文献类型：期刊论文

作者Huang, Jun2,3; Li, Guorong3; Wang, Shuhui1; Xue, Zhe3; Huang, Qingming1,3
刊名NEUROCOMPUTING
出版日期2017-09-27
卷号257页码:164-174
关键词Multi-label classification k nearest neighbors Local label correlation Positive and negative label correlation
ISSN号0925-2312
DOI10.1016/j.neucom.2016.12.073
英文摘要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.
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
语种英语
WOS记录号WOS:000404319800018
出版者ELSEVIER SCIENCE BV
源URL[http://119.78.100.204/handle/2XEOYT63/7096]  
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.Chinese Acad Sci, Key Lab Intelligent Informat Proc, Inst Comp Technol, Beijing 100190, Peoples R China
2.Anhui Univ Technol, Sch Comp Sci & Technol, Maanshan 243032, Peoples R China
3.Univ Chinese Acad Sci, Sch Comp & Control Engn, Beijing 101408, 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.

入库方式： OAI收割

来源：计算技术研究所

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