Recursive gene selection based on maximum margin criterion: a comparison with SVM-RFE

Background: In class prediction problems using microarray data, gene selection is essential to improve the prediction accuracy and to identify potential marker genes for a disease. Among numerous existing methods for gene selection, support vector machine-based recursive feature elimination (SVM-RFE) has become one of the leading methods and is being widely used. The SVM-based approach performs gene selection using the weight vector of the hyperplane constructed by the samples on the margin. However, the performance can be easily affected by noise and outliers, when it is applied to noisy, small sample size microarray data. Results: In this paper, we propose a recursive gene selection method using the discriminant vector of the maximum margin criterion (MMC), which is a variant of classical linear discriminant analysis (LDA). To overcome the computational drawback of classical LDA and the problem of high dimensionality, we present efficient and stable algorithms for MMC-based RFE (MMC...

• Type: Journal
• Year: 2006
• Where: BMCBI
• Authors: Satoshi Niijima, Satoru Kuhara
• Keywords: BMCBI 2006, Gene Selection, Microarray Data, Multi-class Datasets