Evolutionary fuzzy k-nearest neighbors algorithm using interval-valued fuzzy sets

In this work we present a new approach, evolutionary fuzzy k-nearest neighbors classifier using interval-valued fuzzy sets (EF-kNN-IVFS), incorporating interval-valued fuzzy sets for computing the memberships of training instances in fuzzy-kNN. It is based on the representation of multiple choices of two key parameters of fuzzy-kNN: one is applied in the definition of the membership function, and the other is used in the computation of the voting rule. Besides, evolutionary search techniques are incorporated to the model as a self-optimization procedure for setting up these parameters. An experimental study has been carried out to assess the capabilities of our approach. The study has been validated by using nonparametric statistical tests, and remarks the strong performance of EF-kNN-IVFS compared with several state of the art techniques in fuzzy nearest neighbor classification.