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International Workshop on Machine Learning and Data Mining in Pattern Recognition

MLDM 2013: Machine Learning and Data Mining in Pattern Recognition pp 528–542Cite as

DCA Based Algorithms for Feature Selection in Semi-supervised Support Vector Machines

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7988))

Abstract

In this paper, we develop an efficient method for feature selection in Semi-Supervised Support Vector Machine (S3VM). Using an appropriate continuous approximation of the l 0 − norm, we reformulate the feature selection S3VM problem as a DC (Difference of Convex functions) program. DCA (DC Algorithm), an innovative approach in nonconvex programming is then developed to solve the resulting problem. Computational experiments on several real-world datasets show the efficiency and the scalability of our method.

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Author information

Authors and Affiliations

  1. Laboratory of Theoretical and Applied Computer Science - LITA EA 3097, University of Lorraine, Ile du Saulcy, 57045, Metz, France

    Hoai Minh Le, Hoai An Le Thi & Manh Cuong Nguyen

Authors
  1. Hoai Minh Le
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  2. Hoai An Le Thi
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  3. Manh Cuong Nguyen
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Editor information

Editors and Affiliations

  1. Institute of Computer Vision and Applied Computer Sciences, IBaI, Leipzig, Germany

    Petra Perner

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Le, H.M., Le Thi, H.A., Nguyen, M.C. (2013). DCA Based Algorithms for Feature Selection in Semi-supervised Support Vector Machines. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2013. Lecture Notes in Computer Science(), vol 7988. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39712-7_41

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  • DOI: https://doi.org/10.1007/978-3-642-39712-7_41

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-39711-0

  • Online ISBN: 978-3-642-39712-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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