QQ客服 官方微博 反馈留言

	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
	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.

个性服务

查看访问统计

相关权益政策

暂无数据

收藏/分享

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。