Analyzing the Performance of a Multiobjective GA-P Algorithm for Learning Fuzzy Queries in a Machine Learning Environment

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@InProceedings{DBLP:conf/ifsa/CordonHLMZ03,
  author =       "Oscar Cordon and Enrique Herrera-Viedma and 
                 Maria Luque and Felix {de Moya Anegon} and Carmen Zarco",
  title =        "Analyzing the Performance of a Multiobjective GA-P
                 Algorithm for Learning Fuzzy Queries in a Machine
                 Learning Environment",
  booktitle =    "Proceedings of the 10th International Fuzzy Systems
                 Association World Congress, Fuzzy Sets and Systems -
                 IFSA 2003",
  year =         "2003",
  editor =       "Taner Bilgi\c{c} and Bernard De Baets and 
                 Okyay Kaynak",
  publisher =    "Springer",
  series =       "Lecture Notes in Computer Science",
  volume =       "2715",
  pages =        "611--619",
  address =      "Istanbul, Turkey",
  month =        jun # " 30 - " # jul # " 2",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-40383-3",
  URL =          "http://www.scimago.es/publications/ifsa03-cordon.pdf",
  bibsource =    "DBLP, http://dblp.uni-trier.de",
  DOI =          "doi:10.1007/3-540-44967-1_73",
  size =         "9 pages",
  abstract =     "The fuzzy information retrieval model was proposed
                 some years ago to solve several limitations of the
                 Boolean model without a need of a complete redesign of
                 the information retrieval system. However, the
                 complexity of the fuzzy query language makes it
                 difficult to formulate user queries. Among other
                 proposed approaches to solve this problem, we find the
                 Inductive Query by Example (IQBE) framework, where
                 queries are automatically derived from sets of
                 documents provided by the user. In this work we test
                 the applicability of a multiobjective evolutionary IQBE
                 technique for fuzzy queries in a machine learning
                 environment. To do so, the Cranfield documentary
                 collection is divided into two different document sets,
                 labeled training and test, and the algorithm is run on
                 the former to obtain several queries that are then
                 validated on the latter.",
}

Genetic Programming entries for Oscar Cordon Enrique Herrera Viedma Maria Luque Rodriguez Felix de Moya Carmen Zarco Fernandez

Citations