Learning Discriminant Functions based on Genetic Programming and Rough Sets

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

@Article{DBLP:journals/mvl/ChienYH11,
  author =       "Been-Chian Chien and Jui-Hsiang Yang and 
                 Tzung-Pei Hong",
  title =        "Learning Discriminant Functions based on Genetic
                 Programming and Rough Sets",
  journal =      "Multiple-Valued Logic and Soft Computing",
  year =         "2011",
  volume =       "17",
  number =       "2-3",
  pages =        "135--155",
  keywords =     "genetic algorithms, genetic programming, Machine
                 learning, discriminant function, classification, rough
                 sets.",
  ISSN =         "1542-3980",
  URL =          "http://www.oldcitypublishing.com/MVLSC/MVLSCabstracts/MVLSC17.2-3abstracts/MVLSCv17n2-3p135-155Chien.html",
  URL =          "http://www.oldcitypublishing.com/MVLSC/MVLSCcontents/MVLSCv17n2-3contents.html",
  size =         "21 pages",
  abstract =     "Supervised learning based on genetic programming can
                 find different classification models including decision
                 trees, classification rules and discriminant functions.
                 The previous researches have shown that the classifiers
                 learnt by GP have high precision in many application
                 domains. However, nominal data cannot be handled and
                 calculated by the model of using discriminant
                 functions. In this paper, we present a scheme based on
                 rough set theory and genetic programming to learn
                 discriminant functions from general data containing
                 both nominal and numerical attributes. The proposed
                 scheme first transforms the nominal data into numerical
                 values by applying the technique of rough sets. Then,
                 genetic programming is used to learn discriminant
                 functions. The conflict problem among discriminant
                 functions is solved by an effective conflict resolution
                 method based on the distance-based fitness function.
                 The experimental results show that the classifiers
                 generated by the proposed scheme using GP are effective
                 on nominal data in comparison with C4.5, CBA, and
                 NB-based classifiers.",
  notes =        "Oct 2016 oldcitypublishing.com/MVLSC/ in a mess but
                 article on there somewhere...",
  bibsource =    "DBLP, http://dblp.uni-trier.de",
}

Genetic Programming entries for Been-Chian Chien Jui-Hsiang Yang Tzung-Pei Hong

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