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Improving Support Vector Machine Using a Stochastic Local Search for Classification in DataMining

  • Messaouda Nekkaa
  • Dalila Boughaci
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7664)

Abstract

In this paper, an improved support vector machine using a stochastic local search (SVM+SLS) is studied for the classification problem in Datamining. The proposed approach tries to find a subset of features that maximizes the classification accuracy rate of SVM. Experiments on some datasets are performed to show and compare the effectiveness of the proposed approach. The computational experiments show that the proposed SVM+SLS provides competitive results and finds high quality solutions.

Keywords

Support vector machine Stochastic Local Search Combinatorial optimisation Classification Cross validation Feature selection 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Messaouda Nekkaa
    • 1
  • Dalila Boughaci
    • 2
  1. 1.LIMOSE/UMBB- Faculté des sciences BoumerdesBoumerdesAlgérie
  2. 2.LRIA/USTHBBeb-EzzoaurAlgérie

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