title: Improving accuracy of microarray classification by a simple multi-task feature selection filter.
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.
authors.affiliation: Department of Computer and Information Sciences, Temple University, 321 Wachman Hall, 1805 N. Broad Street, Philadelphia, PA 19122, USA. lanliang@temple.edu
authors.forename: Liang
authors.initials: L
authors.lastname: Lan
authors.forename: Slobodan
authors.initials: S
authors.lastname: Vucetic
mesh_terms: D001185:Artificial Intelligence; D019295:Computational Biology; D057225:Data Mining; D030541:Databases, Genetic; D006801:Humans; D016015:Logistic Models; D009369:Neoplasms / Q000235:genetics; D020411:Oligonucleotide Array Sequence Analysis / Q000145:classification* / Q000706:statistics & numerical data*