PSYCH OpenIR  > 脑与认知科学国家重点实验室
Random subspace evidence classifier
Li, Haisheng1; Wen, Guihua1; Yu, Zhiwen1; Zhou, Tiangang2; Wen, GH (reprint author), S China Univ Technol, Sch Comp Sci & Engn, Guangzhou 510006, Guangdong, Peoples R China.
2013
Source PublicationNEUROCOMPUTING
ISSN0925-2312
Subtype期刊论文
Volume110Pages:62-69
Contribution Rank2
AbstractAlthough there exist a lot of k-nearest neighbor approaches and their variants, few of them consider how to make use of the information in both the whole feature space and subspaces. In order to address this limitation, we propose a new classifier named as the random subspace evidence classifier (RSEC). Specifically, RSEC first calculates the local hyperplane distance for each class as the evidences not only in the whole feature space, but also in randomly generated feature subspaces. Then, the basic belief assignment is computed according to these distances for the evidences of each class. In the following, all the evidences represented by basic belief assignments are pooled together by the Dempster's rule. Finally, RSEC assigns the class label to each test sample based on the combined belief assignment. The experiments in the datasets from UCI machine learning repository, artificial data and face image database illustrate that the proposed approach yields lower classification error in average comparing to 7 existing k-nearest neighbor approaches and variants when performing the classification task. In addition, RSEC has good performance in average on the high dimensional data and the minority class of the imbalanced data. (C) 2013 Elsevier B.V. All rights reserved.
KeywordEvidence theory Nearest neighbors Local hyperplane Random subspace
Subject AreaCognitive Psychology
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Indexed BySCI
Language英语
Funding OrganizationChina National Science Foundation [60973083, 61273363, 61003174] ; State Key Laboratory of Brain and Cognitive Science [08812] ; Fundamental Research Funds for the Central Universities, SCUT
Project Intro.The authors thank anonymous reviewers and editors for their valuable suggestions and comments on improving this paper. This work was supported by China National Science Foundation under Grants 60973083, 61273363, 61003174, State Key Laboratory of Brain and Cognitive Science under Grants 08812, and the Fundamental Research Funds for the Central Universities, SCUT.
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000318457700008
WOS HeadingsScience & Technology ; Technology
WOS KeywordNEAREST-NEIGHBOR CLASSIFICATION ; DEMPSTER-SHAFER THEORY ; LOCAL HYPERPLANE ; RECOGNITION ; ALGORITHM ; RULE
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Document Type期刊论文
Identifierhttp://ir.psych.ac.cn/handle/311026/10834
Collection脑与认知科学国家重点实验室
Corresponding AuthorWen, GH (reprint author), S China Univ Technol, Sch Comp Sci & Engn, Guangzhou 510006, Guangdong, Peoples R China.
Affiliation1.S China Univ Technol, Sch Comp Sci & Engn, Guangzhou 510006, Guangdong, Peoples R China
2.Chinese Acad Sci, Inst Psychol, State Key Lab Brain & Cognit Sci, Beijing 100101, Peoples R China
Recommended Citation
GB/T 7714
Li, Haisheng,Wen, Guihua,Yu, Zhiwen,et al. Random subspace evidence classifier[J]. NEUROCOMPUTING,2013,110:62-69.
APA Li, Haisheng,Wen, Guihua,Yu, Zhiwen,Zhou, Tiangang,&Wen, GH .(2013).Random subspace evidence classifier.NEUROCOMPUTING,110,62-69.
MLA Li, Haisheng,et al."Random subspace evidence classifier".NEUROCOMPUTING 110(2013):62-69.
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