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BSO-FS: Bee Swarm Optimization for Feature Selection in Classification

  • Souhila SadegEmail author
  • Leila Hamdad
  • Karima Benatchba
  • Zineb Habbas
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9094)

Abstract

Feature selection is an important data-preprocessing step that often precedes the classification task. Because of large amount of features in real world applications, feature selection is considered as a hard optimization problem. For such problems, metaheuristics have been shown to be a very promising solving approach. In this work, we propose to use Bee Swarm Optimization (BSO) for feature selection. The proposed algorithm, BSO-FS, is based on the wrapper approach that uses BSO for the generation of feature subsets, and a classifier algorithm to evaluate the solutions. BSO-FS is tested on well-known datasets and its performances are compared with those of recently published methods. Obtained results show that for the majority of datasets, BSO-FS selects efficiently relevant features while improving the classification accuracy.

Keywords

Bee Swarm Optimization Metaheuristic Feature selection Wrapper approach Classification Data mining 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Souhila Sadeg
    • 1
    Email author
  • Leila Hamdad
    • 2
  • Karima Benatchba
    • 3
  • Zineb Habbas
    • 4
  1. 1.Ecole nationale Supérieure d’InformatiqueOued Smar, AlgiersAlgeria
  2. 2.LCSIEcole nationale Supérieure d’InformatiqueOued Smar, AlgiersAlgeria
  3. 3.LMCSEcole nationale Supérieure d’InformatiqueOued Smar, AlgiersAlgeria
  4. 4.LCOMSUniversité de LorraineMetzFrance

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