Bioinformatics Vol. 19 Suppl. 2 2003
pages ii179-ii188
© 2003 Oxford University Press
Fast feature selection using a simple estimation of distribution algorithm: a case study on splice site prediction
1 Department of Plant Systems Biology, Ghent
University, Flanders Interuniversity Institute for Biotechnology
(VIB), K.L. Ledeganckstraat 35, Ghent, 9000, Belgium
2 SYSTeMS Research Group, Ghent University,
Technologiepark - Zwijnaarde 9, Zwijnaarde, 9052, Belgium
3 Laboratoire
associéde lINRA (France),
Ghent University, K.L. Ledeganckstraat 35, Ghent, 9000, Belgium
Received on March 17, 2003
; accepted on June 9, 2003
Motivation: Feature subset selection is an important preprocessing step for classification. In biology, where structures or processes are described by a large number of features, the elimination of irrelevant and redundant information in a reasonable amount of time has a number of advantages. It enables the classification system to achieve good or even better solutions with a restricted subset of features, allows for a faster classification, and it helps the human expert focus on a relevant subset of features, hence providing useful biological knowledge.
Results: We present a heuristic method based on Estimation of Distribution Algorithms to select relevant subsets of features for splice site prediction in Arabidopsis thaliana. We show that this method performs a fast detection of relevant feature subsets using the technique of constrained feature subsets. Compared to the traditional greedy methods the gain in speed can be up to one order of magnitude, with results being comparable or even better than the greedy methods. This makes it a very practical solution for classification tasks that can be solved using a relatively small amount of discriminative features (or feature dependencies), but where the initial set of potential discriminative features is rather large.
Keywords: Machine Learning, Feature Subset Selection, Estimation of Distribution Algorithms, Splice Site Prediction.
Contact: yvsae{at}gengenp.rug.ac.be
* To whom correspondence should be addressed.
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