Evolutionary Approach to Overcome Initialization Parameters in Classification Problems

The design of nearest neighbour classifiers is very dependent from some crucial parameters involved in learning, like the number of prototypes to use, the initial localization of these prototypes, and a smoothing parameter. These parameters have to be found by a trial and error process or by some automatic methods. In this work, an evolutionary approach based on Nearest Neighbour Classifier (ENNC), is described. Main property of this algorithm is that it does not require any of the above mentioned parameters. The algorithm is based on the evolution of a set of prototypes that can execute several operators in order to increase their quality in a local sense, and emerging a high classification accuracy for the whole classifier.