title: Feature selection for descriptor based classification models. 1. Theory and GA-SEC algorithm.
abstract: The paper describes different aspects of classification models based on molecular data sets with the focus on feature selection methods. Especially model quality and avoiding a high variance on unseen data (overfitting) will be discussed with respect to the feature selection problem. We present several standard approaches and modifications of our Genetic Algorithm based on the Shannon Entropy Cliques (GA-SEC) algorithm and the extension for classification problems using boosting.
authors.affiliation: Zentrum für Bioinformatik Tübingen (ZBIT), Universität Tübingen, Sand 1, D-72076 Tübingen, Germany. wegnerj@informatik.uni-tuebingen.de
authors.forename: Jörg K
authors.initials: JK
authors.lastname: Wegner
authors.forename: Holger
authors.initials: H
authors.lastname: Fröhlich
authors.forename: Andreas
authors.initials: A
authors.lastname: Zell