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Evolution of superFeatures through genetic programming

Day, Peter ; Nandi, Asoke K.

Expert systems, 2011-05, Vol.28 (2), p.167-184 [Periódico revisado por pares]

Oxford, UK: Blackwell Publishing Ltd

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  • Título:
    Evolution of superFeatures through genetic programming
  • Autor: Day, Peter ; Nandi, Asoke K.
  • Assuntos: binary string fitness characterization ; Classification ; comparative partner selection ; Evolution ; Expert systems ; Genetic algorithms ; genetic programming ; Genetics ; Mathematical programming ; Programming ; Studies ; superFeatures
  • É parte de: Expert systems, 2011-05, Vol.28 (2), p.167-184
  • Notas: ark:/67375/WNG-2MHFJ2XD-F
    ArticleID:EXSY547
    istex:3EA348F35EDFA733858894917A73F90A8F9FBECF
    ObjectType-Article-2
    SourceType-Scholarly Journals-1
    ObjectType-Feature-1
    content type line 23
  • Descrição: : The success of automatic classification is intricately linked with an effective feature selection. Previous studies on the use of genetic programming (GP) to solve classification problems have highlighted its benefits, principally its inherent feature selection (a process that is often performed independent of a learning method). In this paper, the problem of classification is recast as a feature generation problem, where GP is used to evolve programs that allow non‐linear combination of features to create superFeatures, from which classification tasks can be achieved fairly easily. In order to generate superFeatures robustly, the binary string fitness characterization along with the comparative partner selection strategy is introduced with the aim of promoting optimal convergence. The techniques introduced are applied to two illustrative problems first and then to the real‐world problem of audio source classification, with competitive results.
  • Editor: Oxford, UK: Blackwell Publishing Ltd
  • Idioma: Inglês
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  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

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