A bacterial evolutionary algorithm for feature selection

Autoren Mario Drobics
János Botzheim
Titel A bacterial evolutionary algorithm for feature selection
Typ Technischer Bericht
Nummer SCCH-TR-0517
Ort Hagenberg, Austria
Institution SCCH
Jahr 2005
SCCH ID# 517

When creating regression models from data the problem arises that the complexity of the models rapidly increases with the number of features involved. Especially in real world application where a large number of potential features are available, feature selection becomes a crucial task. A novel approach for feature selection is presented which uses a bacterial evolutionary algorithm to identify the optimal set and the optimal number of features with respect to a given learning problem and a given learning algorithm. This method ensures high accuracy and significantly increases interpretability of the resulting models.