A Genetic-Programming-Based Approach for the Learning of Compact Fuzzy Rule-Based Classification Systems

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

@InProceedings{Berlanga:2006:ICAISC,
  author =       "F. J. Berlanga and M. J. {del Jesus} and 
                 M. J. Gacto and F. Herrera",
  title =        "A Genetic-Programming-Based Approach for the Learning
                 of Compact Fuzzy Rule-Based Classification Systems",
  booktitle =    "Proceedings 8th International Conference on Artificial
                 Intelligence and Soft Computing {ICAISC}",
  year =         "2006",
  pages =        "182--191",
  series =       "Lecture Notes on Artificial Intelligence (LNAI)",
  volume =       "4029",
  publisher =    "Springer-Verlag",
  editor =       "Leszek Rutkowski and Ryszard Tadeusiewicz and 
                 Lotfi A. Zadeh and Jacek Zurada",
  address =      "Zakopane, Poland",
  month =        jun # " 25-29",
  bibdate =      "2006-07-05",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/conf/icaisc/icaisc2006.html#BerlangaJGH06",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-35748-3",
  DOI =          "doi:10.1007/11785231_20",
  size =         "10 pages",
  abstract =     "In the design of an interpretable fuzzy rule-based
                 classification system (FRBCS) the precision as much as
                 the simplicity of the extracted knowledge must be
                 considered as objectives. In any inductive learning
                 algorithm, when we deal with problems with a large
                 number of features, the exponential growth of the fuzzy
                 rule search space makes the learning process more
                 difficult. Moreover it leads to an FRBCS with a rule
                 base with a high cardinality. In this paper, we propose
                 a genetic-programming-based method for the learning of
                 an FRBCS, where disjunctive normal form (DNF) rules
                 compete and cooperate among themselves in order to
                 obtain an understandable and compact set of fuzzy
                 rules, which presents a good classification performance
                 with high dimensionality problems. This proposal uses a
                 token competition mechanism to maintain the diversity
                 of the population. The good results obtained with
                 several classification problems support our proposal.",
}

Genetic Programming entries for Francisco Jose Berlanga Maria Jose del Jesus Maria Jose Gacto Francisco Herrera

Citations