Prototype generation techniques have arisen as very competitive methods for enhancing the nearest neighbor classifier through data reduction. Within the prototype generation methodology, the methods of adjusting the prototypes' positioning have shown an outstanding performance. Evolutionary algorithms have been used to optimize the positioning of the prototypes with promising results. However, these results can be improved even more if other data reduction techniques, such as prototype selection and feature weighting, are considered. In this paper, we propose a hybrid evolutionary scheme for data reduction, incorporating a new feature weighting scheme within two different prototype generation methodologies. Specifically, we will focus on a self-adaptive differential evolution algorithm in order to optimize feature weights and the placement of the prototypes. The results are contrasted with nonparametric statistical tests, showing that our proposal outperforms previously proposed methods, thus showing itself to be a suitable tool in the task of enhancing the performance of the nearest neighbor classifier.
['Isaac Triguero', 'Joaquín Derrac', 'Salvador García', 'Francisco Herrera']
Integrating a differential evolution feature weighting scheme into prototype generation