Random subspace evidence classifier
文献类型：期刊论文
| 作者 | Li, Haisheng1; Wen, Guihua1; Yu, Zhiwen1; Zhou, Tiangang2 |
| 刊名 | NEUROCOMPUTING
 |
| 出版日期 | 2013 |
| 卷号 | 110页码:62-69 |
| 关键词 | Evidence theory Nearest neighbors Local hyperplane Random subspace |
| ISSN号 | 0925-2312 |
| 产权排序 | 2 |
| 通讯作者 | Wen, GH (reprint author), S China Univ Technol, Sch Comp Sci & Engn, Guangzhou 510006, Guangdong, Peoples R China. |
| 英文摘要 | 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. |
| WOS标题词 | Science & Technology ; Technology |
| 学科主题 | Cognitive psychology |
| 类目[WOS] | Computer Science, Artificial Intelligence |
| 研究领域[WOS] | Computer Science |
| 关键词[WOS] | NEAREST-NEIGHBOR CLASSIFICATION ; DEMPSTER-SHAFER THEORY ; LOCAL HYPERPLANE ; RECOGNITION ; ALGORITHM ; RULE |
| 收录类别 | SCI |
| 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. |
| 原文出处 | http://ac.els-cdn.com/S0925231213000040/1-s2.0-S0925231213000040-main.pdf?_tid=ae56be6e-cc7f-11e4-86a8-00000aacb35f&acdnat=1426581128_bfbb40cecab7ffb4f11228b8815a4f26 |
| 语种 | 英语 |
| WOS记录号 | WOS:000318457700008 |
| 源URL | [http://ir.psych.ac.cn/handle/311026/10834]  |
| 专题 | 心理研究所_脑与认知科学国家重点实验室 |
| 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 |
| 推荐引用方式 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.(2013).Random subspace evidence classifier.NEUROCOMPUTING,110,62-69. |
| MLA | Li, Haisheng,et al."Random subspace evidence classifier".NEUROCOMPUTING 110(2013):62-69. |
入库方式： OAI收割
来源：心理研究所
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