A multi-objective genetic algorithm for simultaneous model and feature selection for support vector machines

The Support Vector Machines (SVM) constitute a very powerful technique for pattern classification problems. However, its efficiency in practice depends highly on the selection of the kernel function type and relevant parameter values. Selecting relevant features is another factor that can also impact the performance of SVM. The identification of the best set of parameters values for a classification model such as SVM is considered as an optimization problem. Thus, in this paper, we aim to simultaneously optimize SVMs parameters and feature subset using different kernel functions. We cast this problem as a multi-objective optimization problem, where the classification accuracy, the number of support vectors, the margin and the number of selected features define our objective functions. To solve this optimization problem, a method based on multi-objective genetic algorithm NSGA-II is suggested. A multi-criteria selection operator for our NSGA-II is also introduced. The proposed method is tested on some benchmark data-sets. The experimental results show the efficiency of the proposed method where features were reduced and the classification accuracy has been improved.