A Method Based on Genetic Programming for Improving the Quality of Datasets in Classification Problems

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@Article{journals/ijcsa/EstebanezAV07,
  author =       "Cesar Estebanez and Ricardo Aler and 
                 Jose Maria Valls",
  title =        "A Method Based on Genetic Programming for Improving
                 the Quality of Datasets in Classification Problems",
  journal =      "International Journal of Computer Science and
                 Applications",
  year =         "2007",
  volume =       "4",
  number =       "1",
  pages =        "69--80",
  keywords =     "genetic algorithms, genetic programming,
                 Classification, projections",
  ISSN =         "0972-9038",
  URL =          "http://www.tmrfindia.org/ijcsa/V4I17.pdf",
  size =         "12 pages",
  abstract =     "The problem of the representation of data is a key
                 issue in the Machine Learning (ML) field. ML tries to
                 automatically induct knowledge from a set of examples
                 or instances of a problem, learning how to distinguish
                 between the different classes. It is known that
                 inappropriate representations of the data can
                 drastically limit the performance of ML algorithms. On
                 the other hand, a high-quality representation of the
                 same data, can produce a strong improvement in
                 classification rates. In this work we present a
                 GP-based method for automatically evolve projections.
                 These projections change the data space of a
                 classification problem into a higher-quality one, thus
                 improving the performance of ML algorithms. At the same
                 time, our approach can reduce dimensionality by
                 constructing more relevant attributes. We have tested
                 our approach in four domains. The experiments show that
                 it obtains good results, compared to other ML
                 approaches that do not use our projections, while
                 reducing dimensionality in many cases.",
  notes =        "Including Mini Special Issue based on extended
                 versions of selected papers presented during the First
                 International Multiconference on Computer Science and
                 Information Technology (FIMCSIT), which took place in
                 Wisla, Poland, on November 6-10, 2006. Guest Editors:
                 Maria Ganzha and Marcin Paprzycki Ripley Data Set, Pima
                 Indians Diabetes, NIPS 2001 Brain Computer Interface
                 Workshop",
  bibdate =      "2007-10-30",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/journals/ijcsa/ijcsa4.html#EstebanezAV07",
}

Genetic Programming entries for Cesar Estebanez Ricardo Aler Mur Jose Maria Valls Ferran

Citations