Title: Improving accuracy of microarray classification by a simple multi-task feature selection filter
Authors: Liang Lan, Slobodan Vucetic
Addresses: Department of Computer and Information Sciences, Temple University, 321 Wachman Hall, 1805 N. Broad Street, Philadelphia, PA 19122, USA. ' Department of Computer and Information Sciences, Temple University, 304 Wachman Hall, 1805 N. Broad Street, Philadelphia, PA 19122, USA
Abstract: Leveraging information from the publicly accessible data repositories can be very useful when training a classifier from a small-sample microarray data. To achieve this, we proposed a multi-task feature selection filter that borrows strength from auxiliary microarray data. It uses Kruskal–Wallis test on auxiliary data and ranks genes based on their aggregated p-values. The top-ranked genes are selected as features for the target task classifier. The multi-task filter was evaluated on microarray data related to nine different types of cancers. The results showed that the multi-task feature selection is very successful when applied in conjunction with both single-task and multi-task classifiers.
Keywords: feature filters; microarray classification; multi-task learning; transfer learning; bioinformatics; classification accuracy; feature selection.
International Journal of Data Mining and Bioinformatics, 2011 Vol.5 No.2, pp.189 - 208
Published online: 24 Mar 2011 *Full-text access for editors Access for subscribers Purchase this article Comment on this article