Feature selection using bacterial optimization

TitelFeature selection using bacterial optimization
BuchtitelProc. 10th Int. Conf. on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU 2004)
Typin Konferenzband
OrtPerugia, Italy
ISBN88-87242-54-2
MonatJuly
Jahr2004
Seiten797-804
SCCH ID#408
Abstract

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.