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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
发表期刊NEUROCOMPUTING
ISSN0925-2312
文章类型期刊论文
卷号110页码:62-69
产权排序2
摘要Although 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.
关键词Evidence theory Nearest neighbors Local hyperplane Random subspace
学科领域Cognitive Psychology
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收录类别SCI
语种英语
项目资助者China National Science Foundation [60973083, 61273363, 61003174] ; State Key Laboratory of Brain and Cognitive Science [08812] ; Fundamental Research Funds for the Central Universities, SCUT
项目简介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研究方向Computer Science
WOS类目Computer Science, Artificial Intelligence
WOS记录号WOS:000318457700008
WOS标题词Science & Technology ; Technology
关键词[WOS]NEAREST-NEIGHBOR CLASSIFICATION ; DEMPSTER-SHAFER THEORY ; LOCAL HYPERPLANE ; RECOGNITION ; ALGORITHM ; RULE
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文献类型期刊论文
条目标识符http://ir.psych.ac.cn/handle/311026/10834
专题脑与认知科学国家重点实验室
通讯作者Wen, GH (reprint author), S China Univ Technol, Sch Comp Sci & Engn, Guangzhou 510006, Guangdong, Peoples R China.
作者单位1.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
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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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