A robust regression based classifier with determination of optimal feature set

Abstract

This paper proposes a robust regression approach for different classification problems using determination of optimal feature set values. Three different data sets are used to test and evaluate the proposed algorithm. In robust regression stage, the number of vector of regression coefficients is equal to the number of attributes in classification application. In optimization stage, the optimum values of the each of features in classification problem are determined by using genetic algorithm. The high classification accuracy with low number of reference data is the valuable property of proposed method. Simulation results show that proposed classification approach based on robust regression has high accuracy rate. All Rights Reserved © 2015 Universidad Nacional Autónoma de México, Centro de Ciencias Aplicadas y Desarrollo Tecnológico. This is an open access item distributed under the Creative Commons CC License BY-NC-ND 4.0.