NDLI: The Classification of Tumor Using Gene Expression Profile Based on Support Vector Machines and Factor Analysis

Content Provider	IEEE Xplore Digital Library
Author	Shulin Wang Ji Wang Huowang Chen Wensheng Tang
Copyright Year	2006
Description	Author affiliation: Sch. of Comput. Sci., National Univ. of Defense Technol., Changsha (Shulin Wang; Ji Wang; Huowang Chen; Wensheng Tang)
Abstract	Gene expression data that is being used to gather information from tissue samples is expected to significantly improve the development of efficient tumor diagnosis and to provide understanding and insight into tumor related cellular processes. In this paper, we propose a novel feature selection approach which integrates the feature score criterion with factor analysis to further improve the SVM-based classification performance of gene expression data. We examine two sets of published gene expression data to validate the novel feature selection method by means of SVM classifier with different parameters. Experiments show that the proposed hybrid method can select a small quantity of principal factors to represent a large number of genes and SVM has a superior classification performance with the common factors which are extracted from gene expression data. Moreover, experiment results demonstrate successful cross-validation accuracy of 92% for the colon dataset and 100% for the leukemia dataset
Starting Page	471
Ending Page	476
File Size	261206
Page Count	6
File Format	PDF
ISBN	0769525288
DOI	10.1109/ISDA.2006.253882
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-10-16
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	support vector machines Gene expression Data mining classification Neoplasms Information analysis Support vector machines Computer science factor analysis Support vector machine classification DNA Biological data mining gene expression profiles Performance analysis feature selection Principal component analysis
Content Type	Text
Resource Type	Article

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