Some Probabilistic Modelling Ideas For Boolean Classification In Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{muruzabal:2000:pmbcGP,
  author =       "Jorge Muruzabal and Carlos Cotta-Porras and 
                 Amelia Fernandez",
  title =        "Some Probabilistic Modelling Ideas For {Boolean}
                 Classification In Genetic Programming",
  booktitle =    "Genetic Programming, Proceedings of EuroGP'2000",
  year =         "2000",
  editor =       "Riccardo Poli and Wolfgang Banzhaf and 
                 William B. Langdon and Julian F. Miller and Peter Nordin and 
                 Terence C. Fogarty",
  volume =       "1802",
  series =       "LNCS",
  pages =        "133--148",
  address =      "Edinburgh",
  publisher_address = "Berlin",
  month =        "15-16 " # apr,
  organisation = "EvoNet",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-67339-3",
  URL =          "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=1802&spage=133",
  DOI =          "doi:10.1007/978-3-540-46239-2_10",
  abstract =     "We discuss the problem of boolean classification via
                 Genetic Programming. When predictors are numeric, the
                 standard approach proceeds by classifying according to
                 the sign of the value provided by the evaluated
                 function. We consider an alternative approach whereby
                 the magnitude of such a quantity also plays a role in
                 prediction and evaluation. Specifically, the original,
                 unconstrained value is transformed into a probability
                 value which is then used to elicit the classification.
                 This idea stems from the well-known logistic regression
                 paradigm and can be seen as an attempt to squeeze all
                 the information in each individual function. We
                 investigate the empirical behaviour of these variants
                 and discuss a third evaluation measure equally based on
                 probabilistic ideas. To put these ideas in perspective,
                 we present comparative results obtained by alternative
                 methods, namely recursive splitting and logistic
                 regression.",
  notes =        "EuroGP'2000, part of \cite{poli:2000:GP}

                 Carlos Cotta-Porras is Carlos Cotta",
}

Genetic Programming entries for Jorge Muruzabal Carlos Cotta Amelia Fernandez

Citations