A bacterial evolutionary algorithm for feature selection
|Autoren|| Mario Drobics|
|Titel||A bacterial evolutionary algorithm for feature selection|
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.