A centroid-based gene selection method for microarray data classification

	A centroid-based gene selection method for microarray data classification
	Shun Guo; Donghui Guo; Lifei Chen; Qingshan Jiang
刊名	JOURNAL OF THEORETICAL BIOLOGY
	2016
英文摘要	For classification problems based on microarray data, the data typically contains a large number of irrelevant and redundant features. In this paper, a new gene selection method is proposed to choose the best subset of features for microarray data with the irrelevant and redundant features removed. We formulate these lection problemas a L1-regularized optimization problem, based on a newly defined linear discriminant analysis criterion. In stead of calculating the mean of the samples, a kernel-based approach is used to estimate the class centroid to define both the between-class separability and the within-class compactness for the criterion. Theoretic alanalys is indicates that the global optimal solution of the L1-regularized criterion can be reached with a general condition, on which an efficient algorithmis derived to the features election problem in a linear time complexity with respect to the number of features and the number of samples. The experimental results on ten publicly available microarrayda-tasets demon strate that the proposed method performs effectively and competitively compared with state-of-the-art methods.
收录类别	SCI
原文出处	http://www.sciencedirect.com/science/article/pii/S0022519316300108
语种	英语
内容类型	期刊论文
源URL	[http://ir.siat.ac.cn:8080/handle/172644/10270]
专题	深圳先进技术研究院_数字所
作者单位	JOURNAL OF THEORETICAL BIOLOGY
推荐引用方式 GB/T 7714	Shun Guo,Donghui Guo,Lifei Chen,et al. A centroid-based gene selection method for microarray data classification[J]. JOURNAL OF THEORETICAL BIOLOGY,2016.
APA	Shun Guo,Donghui Guo,Lifei Chen,&Qingshan Jiang.(2016).A centroid-based gene selection method for microarray data classification.JOURNAL OF THEORETICAL BIOLOGY.
MLA	Shun Guo,et al."A centroid-based gene selection method for microarray data classification".JOURNAL OF THEORETICAL BIOLOGY (2016).