Evolutionary learning using a sensitivity-accuracy approach for classification

Abstract

Accuracy alone is insufficient to evaluate the performance of a classifier especially when the number of classes increases. This paper proposes an approach to deal with multi-class problems based on Accuracy (C) and Sensitivity (S). We use the differential evolution algorithm and the ELM-algorithm (Extreme Learning Machine) to obtain multi-classifiers with a high classification rate level in the global dataset with an acceptable level of accuracy for each class. This methodology is applied to solve four benchmark classification problems and obtains promising results.