Proposing a Features Preprocessing Method Based on Artificial Immune and Minimum Classification Errors Methods

Abstract

Artificial immune systems that have been inspired from organic immune systems, have drawn many attentions in recent years (and have been considered) as an evolutionary algorithm and have been applied in different papers. This algorithm can be used in two different areas of optimization and classification. In this paper, an artificial immune algorithm has been applied in optimization problem. In particular artificial immune systems have been used for computing the mapping matrices and improving features. Comparison of results of proposed method with other preprocessing methods shows the superiority of the proposed method so that in 90% of cases it has the best performance based on different measures. Evaluation measures are including classification rate, variance and compression measure.