Simultaneous feature selection and classification using kernel-penalized support vector machines |
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Authors: | Sebastiá n Maldonado,Jayanta Basak |
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Affiliation: | Department of Industrial Engineering, University of Chile, República 701, Santiago de Chile, Chile IBM India Research Lab, New Delhi, India |
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Abstract: | We introduce an embedded method that simultaneously selects relevant features during classifier construction by penalizing each feature’s use in the dual formulation of support vector machines (SVM). This approach called kernel-penalized SVM (KP-SVM) optimizes the shape of an anisotropic RBF Kernel eliminating features that have low relevance for the classifier. Additionally, KP-SVM employs an explicit stopping condition, avoiding the elimination of features that would negatively affect the classifier’s performance. We performed experiments on four real-world benchmark problems comparing our approach with well-known feature selection techniques. KP-SVM outperformed the alternative approaches and determined consistently fewer relevant features. |
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Keywords: | Feature selection Embedded methods Support vector machines Mathematical programming |
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