Fast feature selection using a simple Estimation of Distribution Algorithm: A case study on splice site prediction.

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@gengenp.rug.ac.be

Saeys, Y., Degroeve, S., Aeyels, D., Van de Peer, Y. (2003) Fast feature selection using a simple Estimation of Distribution Algorithm: A case study on splice site prediction. Bioinformatics 19 Suppl 2:ii179-88.

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