Elikaei Ahari, M., Nasersharif, B. (2016). Feature Selection Using Multi Objective Genetic Algorithm with Support Vector Machine. Journal of Computer & Robotics, 9(2), 11-17.
Mojgan Elikaei Ahari; Babak Nasersharif. "Feature Selection Using Multi Objective Genetic Algorithm with Support Vector Machine". Journal of Computer & Robotics, 9, 2, 2016, 11-17.
Elikaei Ahari, M., Nasersharif, B. (2016). 'Feature Selection Using Multi Objective Genetic Algorithm with Support Vector Machine', Journal of Computer & Robotics, 9(2), pp. 11-17.
Elikaei Ahari, M., Nasersharif, B. Feature Selection Using Multi Objective Genetic Algorithm with Support Vector Machine. Journal of Computer & Robotics, 2016; 9(2): 11-17.
Feature Selection Using Multi Objective Genetic Algorithm with Support Vector Machine
1Faculty of Computer and Information Technology Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
2Electrical and Computer Engineering Department, K.N. Toosi University of Technology, Iran
Abstract
Different approaches have been proposed for feature selection to obtain suitable features subset among all features. These methods search feature space for feature subsets which satisfies some criteria or optimizes several objective functions. The objective functions are divided into two main groups: filter and wrapper methods. In filter methods, features subsets are selected due to some measures like inter-class distance, features statistical independence or information theoretic measures. Even though, wrapper methods use a classifier to evaluate features subsets by their predictive accuracy (on test data) by statistical resampling or cross-validation. Filter methods usually use only one measure for feature selection that does not necessarily produce the best result. In this paper, we proposed to use the classification error measures besides to filter measures where our classifier is support vector machine (SVM). To this end, we use multi objective genetic algorithm. In this way, one of our feature selection measure is SVM classification error. Another measure is selected between mutual information and Laplacian criteria which indicates informative content and structure preserving property of features, respectively. The evaluation results on the UCI datasets show the efficiency of this method.
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