Ranking-Based Feature Selection Method for Dynamic Belief Clustering

  • Sarra Ben Hariz
  • Zied Elouedi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6943)

Abstract

In this paper, we investigate the problem of dynamic belief clustering. The developed approach tackles the problem of updating the partition by decreasing the attribute set in an uncertain context. We propose a based-ranking feature selection method that allows us to preserve only the most relevant attributes. We deal with uncertainty related to attribute values, which is represented and managed through the Transferable Belief Model (TBM) concepts. The reported results showed that, in general, there is a beneficial effect of using the developed selection method to cluster dynamic feature set in comparison with the other static methods performing a complete reclustering.

Keywords

Feature Selection Feature Selection Method Belief Function Feature Ranking Cluster Partition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Sarra Ben Hariz
    • 1
  • Zied Elouedi
    • 1
  1. 1.LARODEC, Institut Supérieur de Gestion de TunisLe BardoTunisie

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