Two Featuer Transformation Methods Based on Genetic Algorithm for Reducing Support Vector Machine Classification Error

Discriminative methods are used for increasing pattern recognition and classification accuracy. These methods can be used as discriminant transformations applied to features or they can be used as discriminative learning algorithms for the classifiers. Usually, discriminative transformations criteria are different from the criteria of discriminant classifiers training or their error. In this paper, for relating feature transformation criterion to classification rate, we obtain a feature transformation method using genetic algorithm where we choose fitness function as Support Vectomr Machine(SVM) classification error rate. In addition, we obtain a feature transformation method using multi-objective genetic algorithm in order to consider both between class discrimination (According to feature transformation criterion) and support vector machine classification error rate simultaneously. Experimental results on UCI dataset indicate that using both classification error and between class discrimination in feature transformation improve discriminative feature transformations performance in increasing SVM classification accuracy. Additionally, the use of feature transformation with classification error criterion increases SVM classification more than other conventional feature transformation and proposed two-objective methods.