Optimal classifier design method using hierarchical fair competition model based parallel genetic algorithm

By the appropriate editing of the reference set and the judicious selection of features, we can obtain optimal classifier which maximizes the classification accuracy while saving computational time and memory resources. In this paper, a new simultaneous reference set editing and feature selection for an optimal classifier is proposed. Genetic algorithm (GA) based simultaneous editing of the reference set and feature selection to design optimal classifier is receiving attention. However, the problem to find an optimal classifier has very large search spaces. Compared with the simple genetic algorithm (SGA), the hierarchical fair competition parallel genetic algorithm (HFC-PGA) exhibits a promising performance when dealing with huge search spaces, high-dimensionality, and multimodality of the search problems. Therefore, we develop a design methodology for optimal classifier, which deals with simultaneous reference set editing and feature selection using HFC-PGA. Experiments are performed with UCI machine learning repository to show the performance of the proposed algorithm.