Feature selection for splice site prediction: A new method using EDA-based feature ranking

Background: The identification of relevant biological features in large and complex datasets is an important step towards gaining insight in the processes underlying the data. Other advantages of feature selection include the ability of the classification system to attain good or even better solutions using a restricted subset of features, and a faster classification. Thus, robust methods for fast feature selection are of key importance in extracting knowledge from complex biological data. Results: In this paper we present a novel method for feature subset selection applied to splice site prediction, based on estimation of distribution algorithms, a more general framework of genetic algorithms. From the estimated distribution of the algorithm, a feature ranking is derived. Afterwards this ranking is used to iteratively discard features. We apply this technique to the problem of splice site prediction, and show how it can be used to gain insight into the underlying biological process o...

• Type: Journal
• Year: 2004
• Where: BMCBI
• Authors: Yvan Saeys, Sven Degroeve, Dirk Aeyels, Pierre Rouzé, Yves Van de Peer
• Keywords: BMCBI 2004, Distribution Algorithms, Feature Selection, Splice Site