An Improved 1-norm SVM for Simultaneous Classification and Variable Selection

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Hui Zou ;
Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, PMLR 2:675-681, 2007.

Abstract

We propose a novel extension of the 1-norm support vector machine (SVM) for simultaneous feature selection and classification. The new algorithm penalizes the empirical hinge loss by the adaptively weighted 1-norm penalty in which the weights are computed by the 2-norm SVM. Hence the new algorithm is called the hybrid SVM. Simulation and real data examples show that the hybrid SVM not only often improves upon the 1-norm SVM in terms of classification accuracy but also enjoys better feature selection performance.

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