Swarm MeLiF: Feature Selection with Filter Combination Found via Swarm Intelligence

  • Ivan Smetannikov
  • Evgeniy Varlamov
  • Andrey Filchenkov
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 449)

Abstract

Combination of algorithms being called ensemble is a widely used machine learning technique. In this paper we propose a new method Swarm MeLiF which aims to find the best combination of basic filters and uses swarm optimization methods for this purpose. In this work we combine filters by combining the measures they use to evaluate feature importance. Thus, the problem of filter ensemble learning is reduced to finding a linear combination of these measures. We applied several swarm optimization methods and found that Particle Swarm Optimization shows the best results and outperforms the original MeLiF.

Keywords

Swarm intelligence Biologically-inspired meta-heuristics Feature selection Attribute selection Ensemble learning 

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Ivan Smetannikov
    • 1
  • Evgeniy Varlamov
    • 1
  • Andrey Filchenkov
    • 1
  1. 1.ITMO UniversitySt. PetersburgRussia

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