Evolution of Fuzzy Classifiers using Genetic Programming

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

@Article{Muni:2012:FIE,
  author =       "Durga Prasad Muni and Nikhil R. Pal",
  title =        "Evolution of Fuzzy Classifiers using Genetic
                 Programming",
  journal =      "Fuzzy Information and Engineering",
  year =         "2012",
  volume =       "4",
  number =       "1",
  pages =        "29--49",
  month =        mar,
  publisher =    "Springer Berlin Heidelberg, in co-publication with
                 Fuzzy Information and Engineering Branch of the
                 Operations Research Society of China",
  keywords =     "genetic algorithms, genetic programming, Fuzzy logic,
                 Classification, Rule extraction, Evolutionary
                 algorithms",
  ISSN =         "1616-8658",
  DOI =          "doi:10.1007/s12543-012-0099-8",
  size =         "21 pages",
  abstract =     "In this paper, we propose a genetic programming (GP)
                 based approach to evolve fuzzy rule based classifiers.
                 For a c-class problem, a classifier consists of c
                 trees. Each tree, T_i , of the multi-tree classifier
                 represents a set of rules for class i. During the
                 evolutionary process, the inaccurate/inactive rules of
                 the initial set of rules are removed by a cleaning
                 scheme. This allows good rules to sustain and that
                 eventually determines the number of rules. In the
                 beginning, our GP scheme uses a randomly selected
                 subset of features and then evolves the features to be
                 used in each rule. The initial rules are constructed
                 using prototypes, which are generated randomly as well
                 as by the fuzzy k-means (FKM) algorithm. Besides,
                 experiments are conducted in three different ways:
                 Using only randomly generated rules, using a mixture of
                 randomly generated rules and FKM prototype based rules,
                 and with exclusively FKM prototype based rules. The
                 performance of the classifiers is comparable
                 irrespective of the type of initial rules. This
                 emphasises the novelty of the proposed evolutionary
                 scheme. In this context, we propose a new mutation
                 operation to alter the rule parameters. The GP scheme
                 optimises the structure of rules as well as the
                 parameters involved. The method is validated on six
                 benchmark data sets and the performance of the proposed
                 scheme is found to be satisfactory.",
  affiliation =  "Infosys Limited, Bangalore, India",
}

Genetic Programming entries for Durga Prasad Muni Nikhil Ranjan Pal

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