Paper Info
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.
Year
DOI
Venue
2011
10.1504/IJDMB.2011.039177
IJDMB
Keywords
DocType
Volume
simple multi-task feature selection,accessible data repository,auxiliary microarray data,multi-task classifier,microarray classification,microarray data,target task classifier,multi-task feature selection filter,improving accuracy,small-sample microarray data,multi-task feature selection,auxiliary data,multi-task filter
Journal
5
Issue
ISSN
Citations 
2
1748-5673
6
PageRank 
References 
Authors
0.46
17
2
Name
Order
Citations
PageRank
Liang Lan11389.86
Slobodan Vucetic263756.38