A Multi-task Feature Selection Filter for Microarray Classification

A major challenge in microarray classification and biomarker discovery is dealing with small-sample high-dimensional data where the number of genes used as features is typically orders of magnitude larger than the number of labeled microarrays. One way to address this challenge is by leveraging information from the publicly accessible repositories of microarray data. Following this idea, a multi-task feature selection filter is proposed that borrows strength from the auxiliary microarray classification data sets. The filter uses Kruskal-Wallis test on auxiliary data sets and ranks genes based on their aggregated p-values. Expressions of the top-ranked genes are used as features to build a classifier on the target data set. The proposed approach was evaluated on 9 microarray data sets related to 9 different types of cancers. Comparison of the classification accuracies reveals that the multi-task feature selection is superior to single-task feature selection. Furthermore, the results strongly suggest that multi-task algorithms could improve microarray classification by exploiting auxiliary data during feature selection and learning.