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Gene Selection for Microarray Data Classification Using Hybrid Meta-Heuristics

  • Nassima DifEmail author
  • Mohamed walid Attaoui
  • Zakaria Elberrichi
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 64)

Abstract

The hybridization of metaheuristics got a lot of interest lately. The crucial step lies in the choice of the hybrid methods. The major purpose is to make a tradeoff between exploitation and exploration concepts to create a more robust method. Hybrid metaheuristics are used as a solution to many optimization problems such as feature selection. In this paper we propose an hybridization between metaheuristics (PeSOA, FA, DE, AIS, BAT) for a best gene selection in microarray datasets. The main objective is to prove the efficiency of the proposed hybridization compared to the hybrid methods. The experimentations showed that PeSOA-C and HFA were competitive to their hybrid methods, on the other side, AIS-BAT was less promising compared to AIS. As results, we obtained a perfect 100% in case of Leukemia, Ovarian Cancer, Lymphoma and MLL-Leukemia datasets by the HFA hybridization with only 2%–4% of selected genes.

Keywords

Gene selection Feature selection Metaheuristics Hybridization Microarray dataset Classification Data mining PeSOA FA DE AIS BAT 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Nassima Dif
    • 1
    Email author
  • Mohamed walid Attaoui
    • 1
  • Zakaria Elberrichi
    • 1
  1. 1.EEDIS LaboratoryDjillali Liabes UniversitySidi Bel AbbesAlgeria

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