Estimating classifier performance with Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{trujillo2:2011:EuroGP,
  author =       "Leonardo Trujillo and Yuliana Mart\'inez and 
                 Patricia Melin",
  title =        "Estimating classifier performance with Genetic
                 Programming",
  booktitle =    "Proceedings of the 14th European Conference on Genetic
                 Programming, EuroGP 2011",
  year =         "2011",
  month =        "27-29 " # apr,
  editor =       "Sara Silva and James A. Foster and Miguel Nicolau and 
                 Mario Giacobini and Penousal Machado",
  series =       "LNCS",
  volume =       "6621",
  publisher =    "Springer Verlag",
  address =      "Turin, Italy",
  pages =        "274--285",
  organisation = "EvoStar",
  keywords =     "genetic algorithms, genetic programming: poster",
  isbn13 =       "978-3-642-20406-7",
  DOI =          "doi:10.1007/978-3-642-20407-4_24",
  abstract =     "A fundamental task that must be addressed before
                 classifying a set of data, is that of choosing the
                 proper classification method. In other words, a
                 researcher must infer which classifier will achieve the
                 best performance on the classification problem in order
                 to make a reasoned choice. This task is not trivial,
                 and it is mostly resolved based on personal experience
                 and individual preferences. This paper presents a
                 methodological approach to produce estimators of
                 classifier performance, based on descriptive measures
                 of the problem data. The proposal is to use Genetic
                 Programming (GP) to evolve mathematical operators that
                 take as input descriptors of the problem data, and
                 output the expected error that a particular classifier
                 might achieve if it is used to classify the data.
                 Experimental tests show that GP can produce accurate
                 estimators of classifier performance, by evaluating our
                 approach on a large set of 500 two-class problems of
                 multimodal data, using a neural network for
                 classification. The results suggest that the GP
                 approach could provide a tool that helps researchers
                 make a reasoned decision regarding the applicability of
                 a classifier to a particular problem.",
  notes =        "Part of \cite{Silva:2011:GP} EuroGP'2011 held in
                 conjunction with EvoCOP2011 EvoBIO2011 and
                 EvoApplications2011",
}

Genetic Programming entries for Leonardo Trujillo Yuliana Martinez Patricia Melin

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