Feature selection using bacterial optimization

Titel Feature selection using bacterial optimization
Buchtitel Proc. 10th Int. Conf. on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU 2004)
Typ in Konferenzband
Ort Perugia, Italy
ISBN 88-87242-54-2
Monat July
Jahr 2004
Seiten 797-804
SCCH ID# 408

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. In this paper we will present a novel approach to feature selection which uses bacterial optimization to identify the optimal set of features with respect to a given learning problem and a given learning algorithm. This approach ensures high accuracy and significantly increases interpretability of the resulting models.