Author(s)

C.M.N.A. Pereira, R. Schirru & A.S. Martinez

Abstract

This work presents a novel methodology for pattern recognition that uses genetic learning to get an optimized classification system. Each class is represented by several time series in a data base. The idea is to find clusters in the set of the training patterns of each class so that their centroids can distinguish the classes with a minimum of misclassifications. Due to the high level of difficulty found in this optimization problem and the poor prior knowledge about the patterns domain, a model based on genetic algorithm is proposed to extract this knowledge, searching for the minimum number of subclasses that leads to a maximum correctness in the

Keywords