AcademicArticleSCO_84983593663

abstract

• © 2016 - IOS Press and the authors. All rights reserved. K-NN is one of the most popular and effective classifiers nowadays. However, it has some limitations that overcome its applicability in large scale scenarios: basically, it requires storing the whole training set, and it computes distances of a test sample with the training data set. These limitations have been traditionally alleviated with data reduction techniques. This paper introduces a multi-objective evolutionary approach for data reduction. Our method simultaneously generates prototypes and selects features for k-NN classifiers. Contrary to most of the existing approaches, our method treats the problem with multiobjective evolutionary optimizers. We show the effectiveness of our proposal in benchmark data and compare its performance with state of the art techniques.