Boost feature subset selection: A new gene selection algorithm for microarray dataset

Abstract

Gene selection is usually the crucial first step in microarray data analysis. One class of typical approaches is to calculate some discriminative scores using data associated with a single gene. Such discriminative scores are then sorted and top ranked genes are selected for further analysis. However, such an approach will result in redundant gene set since it ignores the complex relation-ships between genes. Recent researches in feature subset selection began to tackle this problem by limiting the correlations of the selected feature set. In this paper, we propose a novel general framework BFSS: Boost Feature Subset Selection to improve the performance of single-gene based discriminative scores using boot-strapping techniques. Features are selected from dynamically adjusted bootstraps of the training dataset. We tested our algorithm on three well-known publicly available microarray data sets in the bioinformatics community. Encouraging results are reported in this paper. © Springer-Verlag Berlin Heidelberg 2006.