Multi-objective unsupervised feature selection algorithm utilizing redundancy measure and negative epsilon-dominance for fault diagnosis | |
Xia, Hu; Zhuang, Jian; Yu, Dehong | |
刊名 | NEUROCOMPUTING
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2014 | |
卷号 | 146期号:[db:dc_citation_issue]页码:113-124 |
关键词 | Fault recognition Multi-objective evolutionary algorithm Redundancy measure Feature selection Negative epsilon-dominance |
ISSN号 | 0925-2312 |
DOI | [db:dc_identifier_doi] |
URL标识 | 查看原文 |
WOS记录号 | [DB:DC_IDENTIFIER_WOSID] |
内容类型 | 期刊论文 |
URI标识 | http://www.corc.org.cn/handle/1471x/3301971 |
专题 | 西安交通大学 |
推荐引用方式 GB/T 7714 | Xia, Hu,Zhuang, Jian,Yu, Dehong. Multi-objective unsupervised feature selection algorithm utilizing redundancy measure and negative epsilon-dominance for fault diagnosis[J]. NEUROCOMPUTING,2014,146([db:dc_citation_issue]):113-124. |
APA | Xia, Hu,Zhuang, Jian,&Yu, Dehong.(2014).Multi-objective unsupervised feature selection algorithm utilizing redundancy measure and negative epsilon-dominance for fault diagnosis.NEUROCOMPUTING,146([db:dc_citation_issue]),113-124. |
MLA | Xia, Hu,et al."Multi-objective unsupervised feature selection algorithm utilizing redundancy measure and negative epsilon-dominance for fault diagnosis".NEUROCOMPUTING 146.[db:dc_citation_issue](2014):113-124. |
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