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)|
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.