Improving accuracy of microarray classification by a simple multi-task feature selection filter
by Liang Lan, Slobodan Vucetic
International Journal of Data Mining and Bioinformatics (IJDMB), Vol. 5, No. 2, 2011

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.

Online publication date: Sat, 24-Jan-2015

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