A Multi-dimensional Genetic Programming Approach for Multi-class Classification Problems

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "Vijay Ingalalli and Sara Silva and Mauro Castelli and 
                 Leonardo Vanneschi",
  title =        "A Multi-dimensional Genetic Programming Approach for
                 Multi-class Classification Problems",
  booktitle =    "17th European Conference on Genetic Programming",
  year =         "2014",
  editor =       "Miguel Nicolau and Krzysztof Krawiec and 
                 Malcolm I. Heywood and Mauro Castelli and Pablo Garcia-Sanchez and 
                 Juan J. Merelo and Victor M. {Rivas Santos} and 
                 Kevin Sim",
  series =       "LNCS",
  volume =       "8599",
  publisher =    "Springer",
  pages =        "48--60",
  address =      "Granada, Spain",
  month =        "23-25 " # apr,
  organisation = "EvoStar",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-662-44302-6",
  DOI =          "doi:10.1007/978-3-662-44303-3_5",
  abstract =     "Classification problems are of profound interest for
                 the machine learning community as well as to an array
                 of application fields. However, multi-class
                 classification problems can be very complex, in
                 particular when the number of classes is high. Although
                 very successful in so many applications, GP was never
                 regarded as a good method to perform multi-class
                 classification. In this work, we present a novel
                 algorithm for tree based GP, that incorporates some
                 ideas on the representation of the solution space in
                 higher dimensions. This idea lays some foundations on
                 addressing multi-class classification problems using
                 GP, which may lead to further research in this
                 direction. We test the new approach on a large set of
                 benchmark problems from several different sources, and
                 observe its competitiveness against the most successful
                 state-of-the-art classifiers.",
  notes =        "Part of \cite{Nicolau:2014:GP} EuroGP'2014 held in
                 conjunction with EvoCOP2014, EvoBIO2014, EvoMusArt2014
                 and EvoApplications2014",

Genetic Programming entries for Vijay Ingalalli Sara Silva Mauro Castelli Leonardo Vanneschi