An Optimization-Based Framework for Feature Selection and Parameters Determination of SVMs

Автор: Seyyid Ahmed Medjahed, Mohammed Ouali, Tamazouzt Ait Saadi, Abdelkader Benyettou

Журнал: International Journal of Information Technology and Computer Science(IJITCS) @ijitcs

Статья в выпуске: 5 Vol. 7, 2015 года.

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In this paper, feature selection and parameters determination in SVM are cast as an energy minimization procedure. The problem of feature selection and parameters determination is a very difficult problem where the number of feature is very large and where the features are highly correlated. We define the problem of feature selection and parameters determination in SVM as a combinatorial problem and we use a stochastic method that, theoretically, guarantees to reach the global optimum. Several public datasets are employed to evaluate the performance of our approach. Also, we propose to use the DNA Microarray Datasets which are characterized by the large number of features. To validate our approach, we apply it to image classification. The feature descriptors of the images were extracted by using the Pyramid Histogram of Oriented Gradients. The proposed approach was compared with twenty feature selection methods. Experimental results indicate that the classification accuracy rates of the proposed approach exceed those of other approaches.

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Feature Selection, Parameter Determination, Learning Set Selection, Support Vector Machine, Simulated Annealing

Короткий адрес: https://sciup.org/15012278

IDR: 15012278

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