Improving Relevance Measures Using Genetic Programming

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

  author =       "Kourosh Neshatian and Mengjie Zhang",
  title =        "Improving Relevance Measures Using Genetic
  booktitle =    "Proceedings of the 15th European Conference on Genetic
                 Programming, EuroGP 2012",
  year =         "2012",
  month =        "11-13 " # apr,
  editor =       "Alberto Moraglio and Sara Silva and 
                 Krzysztof Krawiec and Penousal Machado and Carlos Cotta",
  series =       "LNCS",
  volume =       "7244",
  publisher =    "Springer Verlag",
  address =      "Malaga, Spain",
  pages =        "97--108",
  organisation = "EvoStar",
  isbn13 =       "978-3-642-29138-8",
  DOI =          "doi:10.1007/978-3-642-29139-5_9",
  keywords =     "genetic algorithms, genetic programming, Relevance
                 measure, Binary classification, Multivariate dependency
  abstract =     "Relevance is a central concept in many feature
                 selection algorithms. Given a relevance measure, a
                 feature selection algorithm searches for a subset of
                 features that maximise the relevance between the subset
                 and target concepts. This paper first shows how
                 relevance measures that rely on the posterior
                 estimation such as information theory measures may fail
                 to quantify the actual utility of subsets of features
                 in certain situations. The paper then proposes a
                 solution based on Genetic Programming which can improve
                 the usability of these measures. The paper is focused
                 on classification problems with numeric features.",
  notes =        "Part of \cite{Moraglio:2012:GP} EuroGP'2012 held in
                 conjunction with EvoCOP2012 EvoBIO2012, EvoMusArt2012
                 and EvoApplications2012",

Genetic Programming entries for Kourosh Neshatian Mengjie Zhang