Institutional Repository, Institute of Psychology, Chinese Academy of Sciences
Random subspace evidence classifier | |
Li, Haisheng1; Wen, Guihua1; Yu, Zhiwen1; Zhou, Tiangang2 | |
通讯作者邮箱 | crghwen@scut.edu.cn |
心理所单位排序 | 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 |
2013 | |
语种 | 英语 |
DOI | 10.1016/j.neucom.2012.11.019 |
发表期刊 | NEUROCOMPUTING |
ISSN | 0925-2312 |
卷号 | 110页码:62-69 |
期刊论文类型 | 期刊论文 |
URL | 查看原文 |
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. |
WOS关键词 | NEAREST-NEIGHBOR CLASSIFICATION ; DEMPSTER-SHAFER THEORY ; LOCAL HYPERPLANE ; RECOGNITION ; ALGORITHM ; RULE |
WOS标题词 | Science & Technology ; Technology |
WOS研究方向 | Computer Science |
WOS类目 | Computer Science, Artificial Intelligence |
WOS记录号 | WOS:000318457700008 |
Q分类 | Q1 |
资助机构 | China National Science Foundation [60973083, 61273363, 61003174] ; State Key Laboratory of Brain and Cognitive Science [08812] ; Fundamental Research Funds for the Central Universities, SCUT |
引用统计 | |
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, Guihua |
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. |
条目包含的文件 | ||||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
WOS_000318457700008.(630KB) | 期刊论文 | 出版稿 | 限制开放 | CC BY-NC-SA | 请求全文 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论