Self-configuring hybrid evolutionary algorithm for fuzzy classification with active learning

A novel approach for active training example selection in classification problems is presented. This active selection of training examples is designed to decrease the amount of computation resources required and increase the classification quality achieved. The approach changes the training sample during the evolutionary process so that the algorithm concentrates on problematic instances that are hard to classify. A fuzzy classifier designed with a self-configuring modification of a hybrid evolutionary algorithm is applied as a classification problem solver. The benchmark containing 9 data sets from KEEL is used to prove the usefulness of the approach proposed.