A binary-constrained Geometric Semantic Genetic Programming for feature selection purposes

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@Article{journals/prl/PapaRP17,
  author =       "Joao Paulo Papa and Gustavo Henrique Rosa and 
                 Luciene Patrici Papa",
  title =        "A binary-constrained Geometric Semantic Genetic
                 Programming for feature selection purposes",
  journal =      "Pattern Recognition Letters",
  year =         "2017",
  volume =       "100",
  pages =        "59--66",
  month =        "1 " # dec,
  keywords =     "genetic algorithms, genetic programming, feature
                 selection, geometric semantic genetic programming,
                 optimum-path forest",
  ISSN =         "0167-8655",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0167865517303641",
  DOI =          "doi:10.1016/j.patrec.2017.10.002",
  bibdate =      "2017-11-29",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/journals/prl/prl100.html#PapaRP17",
  abstract =     "Feature selection concerns the task of finding the
                 subset of features that are most relevant to some
                 specific problem in the context of machine learning. By
                 selecting proper features, one can reduce the
                 computational complexity of the learnt model, and to
                 possibly enhance its effectiveness by reducing the
                 well-known overfitting. During the last years, the
                 problem of feature selection has been modelled as an
                 optimisation task, where the idea is to find the subset
                 of features that maximise some fitness function, which
                 can be a given classifier's accuracy or even some
                 measure concerning the samples' separability in the
                 feature space, for instance. In this paper, we
                 introduced Geometric Semantic Genetic Programming
                 (GSGP) in the context of feature selection, and we
                 experimentally showed it can work properly with both
                 conic and non-conic fitness landscapes. We observed
                 that there is no need to restrict the feature selection
                 modelling into GSGP constraints, which can be quite
                 useful to adopt the semantic operators to a broader
                 range of applications.",
  notes =        "Also known as \cite{PAPA201759}",
}

Genetic Programming entries for Joao Paulo Papa Gustavo Rosa Luciene Patrici Papa

Citations