A preliminary study on the use of differential evolution for adjusting the position of examples in nearest neighbor classification

Abstract

Nearest neighbor is one of the most successfully used techniques for performing classification and pattern recognition tasks. Its simplicity and effectiveness justify the use of this technique in certain domains but it however presents several drawbacks referring to time response, noise sensitivity and storage requirements. Several solutions have been proposed in order to alleviate these problems, such as improving the technique for speeding up or carrying out a data reduction process. Prototype generation is a suitable process for data reduction that allows to fit a data set for nearest neighbor classification. Position adjustment of prototypes is a successful technique within the prototype generation methodology. Evolutionary algorithms are adaptive methods based on natural evolution that may be used for search and optimization. Position adjustment of prototypes can be viewed as a search problem, thus it could be solved using evolutionary algorithms. In this paper, we perform a preliminary study on the use of differential evolution algorithms to the prototype generation problem. Differential evolution models are compared with other algorithms for adjusting the position of prototypes and the results are contrasted through non-parametrical statistical tests. The results show that some differential evolution models consistently outperform previously proposed methods.