A multiple kernel support vector machine scheme for feature selection and rule extraction from gene expression data of cancer tissue

CORC > 科技战略咨询研究院 > 中国科学院科技战略咨询研究院 > 中国科学院科技政策与管理科学研究所(1985年6月-2016年1月19日)

	A multiple kernel support vector machine scheme for feature selection and rule extraction from gene expression data of cancer tissue
	Chen, ZY; Li, JP; Wei, LW
刊名	ARTIFICIAL INTELLIGENCE IN MEDICINE
	2007
卷号	41 期号:2 页码:15,161-175
关键词	Multiple Kernel Learning Support Vector Machine Feature Selection Rule Extraction Gene Expression Data
ISSN号	0933-3657
英文摘要	Objective: Recently, gene expression profiling using microarray techniques has been shown as a promising tool to improve the diagnosis and treatment of cancer. Gene expression data contain high level. of noise and the overwhelming number of genes relative to the number of available samples. It brings out a great challenge for machine learning and statistic techniques. Support vector machine (SVM) has been successfully used to classify gene expression data of cancer tissue. In the medical field, it is crucial to deliver the user a transparent decision process. How to explain the computed solutions and present the extracted knowledge becomes a main obstacle for SVM. Material and methods: A multiple kernel support vector machine (MK-SVM) scheme, consisting of feature selection, rule extraction and prediction modeling is proposed to improve the explanation capacity of SVM. In this scheme, we show that the feature selection problem can be translated into an ordinary multiple parameters learning problem. And a shrinkage approach: 1-norm based linear programming is proposed to obtain the sparse parameters and the corresponding selected features. We propose a novel rule extraction approach using the information provided by the separating hyperplane and support vectors to improve the generalization capacity and comprehensibility of rules and reduce the computational complexity. Results and conclusion: Two public gene expression datasets: leukemia dataset and colon tumor dataset are used to demonstrate the performance of this approach. Using the small number of selected genes, MK-SVM achieves encouraging classification accuracy: more than 90% for both two datasets. Moreover, very simple rules with linguist labels are extracted. The rule sets have high diagnostic power because of their good classification performance. (C) 2007 Elsevier B.V. All rights reserved.
学科主题	Computer Science ; Artificial Intelligence ; Biomedical ; Engineering ; Medical Informatics
语种	英语
WOS记录号	WOS:000250714000008
公开日期	2012-11-12
内容类型	期刊论文
源URL	[http://ir.casipm.ac.cn/handle/190111/4837]
专题	科技战略咨询研究院_中国科学院科技政策与管理科学研究所(1985年6月-2015年12月)
推荐引用方式 GB/T 7714	Chen, ZY,Li, JP,Wei, LW. A multiple kernel support vector machine scheme for feature selection and rule extraction from gene expression data of cancer tissue[J]. ARTIFICIAL INTELLIGENCE IN MEDICINE,2007,41(2):15,161-175.
APA	Chen, ZY,Li, JP,&Wei, LW.(2007).A multiple kernel support vector machine scheme for feature selection and rule extraction from gene expression data of cancer tissue.ARTIFICIAL INTELLIGENCE IN MEDICINE,41(2),15,161-175.
MLA	Chen, ZY,et al."A multiple kernel support vector machine scheme for feature selection and rule extraction from gene expression data of cancer tissue".ARTIFICIAL INTELLIGENCE IN MEDICINE 41.2(2007):15,161-175.