Use of Linear Genetic Programming and Artificial Neural Network Methods to Solve Classification Task

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@Article{journals/riga/ProvorovsB11,
  author =       "Sergejs Provorovs and Arkady Borisov",
  title =        "Use of Linear Genetic Programming and Artificial
                 Neural Network Methods to Solve Classification Task",
  journal =      "Journal Riga Technical University",
  year =         "2011",
  volume =       "45",
  pages =        "133--138",
  publisher =    "Versita, Warsaw",
  keywords =     "genetic algorithms, genetic programming, linear
                 genetic programming, artificial neural networks,
                 classification task, cross-validation",
  ISSN =         "1407-7493",
  URL =          "http://versita.metapress.com/content/v747725r3q041610/fulltext.pdf",
  DOI =          "doi:10.2478/v10143-011-0055-9",
  size =         "6 pages",
  abstract =     "This paper presents a comparative analysis of linear
                 genetic programming and artificial neural network
                 methods to solve classification tasks. Usually
                 classification tasks have data sets containing a large
                 number of attributes and records, and more than two
                 classes that will be processed using, for example,
                 created classification rules. As a result, by using
                 classical method to classify a large number of records,
                 a high classification error value will be obtained. The
                 artificial neural networks are often used to solve
                 classification task, mostly obtaining good results. The
                 linear genetic programming is a new direction of
                 evolution algorithms that is not widely researched and
                 its application areas are not well defined. However,
                 some advantages of linear genetic programming are based
                 on genetic operators whose structure does not require
                 complicated calculations.

                 During this work approximately 400 experiments were
                 conducted with linear genetic programming and
                 artificial neural network methods, using various data
                 sets with different quantity of records, attributes and
                 classes.

                 Based on the results received, conclusions on
                 possibilities of using the methods of linear genetic
                 programming and artificial neural networks in
                 classification problems were drawn, and suggestions for
                 improving their performance were proposed.",
  notes =        "UCI: Iris, Glass, Heart, Zoo, Abalone",
  bibdate =      "2012-03-07",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/journals/riga/riga45.html#ProvorovsB11",
}

Genetic Programming entries for Sergejs Provorovs Arkady Borisov

Citations