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Semi-supervised SVM-based Feature Selection for Cancer Classification using Microarray Gene Expression Data

Ang, Jun Chin and Haron, Habibollah and Abdull Hamed, Haza Nuzly (2015) Semi-supervised SVM-based Feature Selection for Cancer Classification using Microarray Gene Expression Data. CURRENT APPROACHES IN APPLIED ARTIFICIAL INTELLIGENCE, 9101 . pp. 468-477. ISSN 0302-9743

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Abstract

Gene expression data always suffer from the high dimensionality issue, therefore feature selection becomes a fundamental tool in the analysis of cancer classification. Basically, the data can be collected easily without providing the label information, which is quite useful in improving the accuracy of the classification. Label information usually difficult to obtain as the labelling processes are tedious, costly and error prone. Previous studies of gene selection are mostly dedicated to supervised and unsupervised approaches. Support vector machine (SVM) is a common supervised technique to address gene selection and cancer classification problems. Hence, this paper aims to propose a semi-supervised SVM-based feature selection (S3VM-FS), which simultaneously exploit the knowledge from unlabelled and labelled data. Experimental results on the gene expression data of lung cancer show that S3VM-FS achieves the higher accuracy yet requires shorter processing time compares with the well-known supervised method, SVM-based recursive feature elimination (SVM-RFE) and the improved method, S3VM-RFE.

Item Type:Article
Subjects:A General Works
ID Code:59470
Deposited By: Haliza Zainal
Deposited On:18 Jan 2017 09:50
Last Modified:20 Jun 2017 09:29

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