Multiclass classification and gene selection with a stochastic algorithm

Kim-Anh Lê Cao, Agnès Bonnet and Sébastien Gadat ()

Computational Statistics & Data Analysis, 2009, vol. 53, issue 10, 3601-3615

Abstract: Microarray technology allows for the monitoring of thousands of gene expressions in various biological conditions, but most of these genes are irrelevant for classifying these conditions. Feature selection is consequently needed to help reduce the dimension of the variable space. Starting from the application of the stochastic meta-algorithm "Optimal Feature Weighting" (OFW) for selecting features in various classification problems, focus is made on the multiclass problem that wrapper methods rarely handle. From a computational point of view, one of the main difficulties comes from the unbalanced classes situation that is commonly encountered in microarray data. From a theoretical point of view, very few methods have been developed so far to minimize the classification error made on the minority classes. The OFW approach is developed to handle multiclass problems using CART and one-vs-one SVM classifiers. Comparisons are made with other multiclass selection algorithms such as Random Forests and the filter method F-test on five public microarray data sets with various complexities. Statistical relevancy of the gene selections is assessed by computing the performances and the stability of these different approaches and the results obtained show that the two proposed approaches are competitive and relevant to selecting genes classifying the minority classes. Application to a pig folliculogenesis study follows and a detailed interpretation of the genes that were selected shows that the OFW approach answers the biological question.

Date: 2009
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