Robust feature selection for SVMs under uncertain data

Authors:
Hoai An Le Thi;Xuan Thanh Vo;Tao Pham Dinh
Affiliations:
Laboratory of Theoretical and Applied Computer Science EA 3097, University of Lorraine, Metz, France,Lorraine Research Laboratory in Computer Science and Its Applications, CNRS UMR 7503, Universit ...;Laboratory of Theoretical and Applied Computer Science EA 3097, University of Lorraine, Metz, France;Laboratory of Mathematics, National Institute for Applied Sciences-Rouen, Saint-Etienne-du-Rouvray cedex, France
Venue:
ICDM'13 Proceedings of the 13th international conference on Advances in Data Mining: applications and theoretical aspects
Year:
2013

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Abstract

In this paper, we consider the problem of feature selection and classification under uncertain data that is inherently prevalent in almost all datasets. Using principles of Robust Optimization, we propose a robust scheme to handle data with ellipsoidal model uncertainty. The difficulty in treating zero-norm ℓ0 in feature selection problem is overcome by using an appropriate approximation and DC (Difference of Convex functions) programming and DCA (DC Algorithm). The computational results show that the proposed robust optimization approach is more performant than a traditional approach in immunizing perturbation of the data.