NDLI: A Multi-task Feature Selection Filter for Microarray Classification

Content Provider	IEEE Xplore Digital Library
Author	Liang Lan Vucetic, S.
Copyright Year	2009
Abstract	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.
Starting Page	160
Ending Page	165
File Size	253758
Page Count	6
File Format	PDF
ISBN	9780769538853
DOI	10.1109/BIBM.2009.79
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-11-01
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	microarray classification Brain transfer learning Extraterrestrial measurements Vectors Classification algorithms Neoplasms multi-task learning Filters Training data feature selection Logistics Testing Cancer
Content Type	Text
Resource Type	Article

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