Automating the Design of Data Mining Algorithms

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@Book{Pappa:AEDMA,
  author =       "Gisele L. Pappa and Alex A. Freitas",
  title =        "Automating the Design of Data Mining Algorithms",
  subtitle =     "An Evolutionary Computation Approach",
  publisher =    "Springer",
  year =         "2010",
  volume =       "XIII",
  series =       "Natural Computing Series",
  ISSN =         "1619-7127",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-642-02540-2",
  URL =          "http://www.springerlink.com/content/978-3-642-02540-2",
  DOI =          "doi:10.1007/978-3-642-02541-9",
  abstract =     "Traditionally, evolutionary computing techniques have
                 been applied in the area of data mining either to
                 optimize the parameters of data mining algorithms or to
                 discover knowledge or patterns in the data, i.e., to
                 directly solve the target data mining problem. This
                 book proposes a different goal for evolutionary
                 algorithms in data mining: to automate the design of a
                 data mining algorithm, rather than just optimize its
                 parameters.

                 The authors first offer introductory overviews on data
                 mining, focusing on rule induction methods, and on
                 evolutionary algorithms, focusing on genetic
                 programming. They then examine the conventional use of
                 evolutionary algorithms to discover classification
                 rules or related types of knowledge structures in the
                 data, before moving to the more ambitious objective of
                 their research, the design of a new genetic programming
                 system for automating the design of full rule induction
                 algorithms. They analyze computational results from
                 their automatically designed algorithms, which show
                 that the machine-designed rule induction algorithms are
                 competitive when compared with state-of-the-art
                 human-designed algorithms. Finally the authors examine
                 future research directions.

                 This book will be useful for researchers and
                 practitioners in the areas of data mining and
                 evolutionary computation.",
  notes =        "

                 Reviewed in \cite{Woodward:2011:GPEM}",
  size =         "187 pages",
}

Genetic Programming entries for Gisele L Pappa Alex Alves Freitas

Citations