NDLI: Gene selection algorithm combining ReliefF and relative neighborhood rough set

Content Provider	IEEE Xplore Digital Library
Author	Jiu-cheng Xu Ling-jun Zhang Lin Sun Yun-peng Gao
Copyright Year	2011
Description	Author affiliation: College of Computer & Information Technology, Henan Normal University, Xinxiang Henan 453007, China (Jiu-cheng Xu; Ling-jun Zhang; Yun-peng Gao) \|\| College of Computer & Information Technology, Henan Normal, University, Xinxiang, Henan 453007, China (Lin Sun)
Abstract	The curse of dimensionality, caused by high-dimensionality gene and small-size sample of gene expression dataset, may degrade the accuracy of tumor classification. To solve the issue, in this paper, the neighborhood rough set theory is introduced, and through expanding neighborhood threshold, the relative neighborhood rough set theory is proposed. Some corresponding theorems are drawn, and a gene selection algorithm of multi-class problem, by combining ReliefF and relative neighborhood rough set, is constructed. Finally, through comparing with other methods on four opened gene expression datasets, our method shows that only few genes could achieve higher tumor classification accuracy.
Starting Page	745
Ending Page	749
File Size	213541
Page Count	5
File Format	PDF
ISBN	9781457703720
e-ISBN	9781457703713
DOI	10.1109/GRC.2011.6122691
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-11-08
Publisher Place	Taiwan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computers Neighborhood threshold Diversity reception Educational institutions Set theory Classification algorithms Gene selection Relative neighborhood rough Gene expression ReliefF Tumors
Content Type	Text
Resource Type	Article

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