Automating the Design of Data Mining Algorithms with Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3949

@InProceedings{Freitas:2013:NICSO,
  author =       "Alex A. Freitas",
  title =        "Automating the Design of Data Mining Algorithms with
                 Genetic Programming",
  booktitle =    "VI International Workshop on Nature Inspired
                 Cooperative Strategies for Optimization (NICSO 2013)",
  year =         "2013",
  editor =       "German Terrazas and Fernando Esteban Barril Otero and 
                 Antonio D. Masegosa",
  volume =       "512",
  series =       "Studies in Computational Intelligence",
  pages =        "ix",
  address =      "Canterbury, United Kingdom",
  month =        sep # " 2-4",
  publisher =    "Springer",
  note =         "Plenary Talk",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-319-01691-7",
  URL =          "http://link.springer.com/content/pdf/bfm%3A978-3-319-01692-4%2F1.pdf",
  DOI =          "doi:10.1007/978-3-319-01692-4",
  size =         "0.5 pages",
  abstract =     "Rule induction and decision-tree induction algorithms
                 are among the most popular types of classification
                 algorithms in the field of data mining. Research on
                 these two types of algorithms produced many new
                 algorithms in the last 30 years. However, all the rule
                 induction and decision-tree induction algorithms
                 created over that period have in common the fact that
                 they have been manually designed, typically by
                 incrementally modifying a few basic rule induction or
                 decision-tree induction algorithms. Having these basic
                 algorithms and their components in mind, we describe
                 the use of Genetic Programming (GP), a type of
                 evolutionary algorithm that automatically creates
                 computer programs, to automate the process of designing
                 rule induction and decision-tree induction algorithms.
                 The basic motivation is to automatically create
                 complete rule induction and decision-tree induction
                 algorithms in a data-driven way, trying to avoid the
                 human biases and preconceptions incorporated in
                 manually-designed algorithms. Two proposed GP methods
                 (one for evolving rule induction algorithms, the other
                 for evolving decision-tree induction algorithms) are
                 evaluated on a number of datasets, and the results show
                 that the machine-designed rule induction and
                 decision-tree induction algorithms are competitive with
                 well-known human-designed algorithms of the same
                 type.",
  notes =        "1 page abstract only \cite{Pappa:AEDMA}

                 See also \cite{Vanneschi:2013:NICSO}. NICSO 2013
                 http://www.nicso2013.org/programme.html
                 http://www.springer.com/engineering/computational+intelligence+and+complexity/book/978-3-319-01691-7",
}

Genetic Programming entries for Alex Alves Freitas

Citations