Using Genetic Programming to Obtain Implicit Diversity

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

@InProceedings{Johansson:2009:cec,
  author =       "Ulf Johansson and Cecilia Sonstrod and 
                 Tuve Lofstrom and Rikard Konig",
  title =        "Using Genetic Programming to Obtain Implicit
                 Diversity",
  booktitle =    "2009 IEEE Congress on Evolutionary Computation",
  year =         "2009",
  editor =       "Andy Tyrrell",
  pages =        "2454--2459",
  address =      "Trondheim, Norway",
  month =        "18-21 " # may,
  organization = "IEEE Computational Intelligence Society",
  publisher =    "IEEE Press",
  isbn13 =       "978-1-4244-2959-2",
  file =         "P558.pdf",
  DOI =          "doi:10.1109/CEC.2009.4983248",
  abstract =     "When performing predictive data mining, the use of
                 ensembles is known to increase prediction accuracy,
                 compared to single models. To obtain this higher
                 accuracy, ensembles should be built from base
                 classifiers that are both accurate and diverse. The
                 question of how to balance these two properties in
                 order to maximize ensemble accuracy is, however, far
                 from solved and many different techniques for obtaining
                 ensemble diversity exist. One such technique is
                 bagging, where implicit diversity is introduced by
                 training base classifiers on different subsets of
                 available data instances, thus resulting in less
                 accurate, but diverse base classifiers. In this paper,
                 genetic programming is used as an alternative method to
                 obtain implicit diversity in ensembles by evolving
                 accurate, but different base classifiers in the form of
                 decision trees, thus exploiting the inherent
                 inconsistency of genetic programming. The experiments
                 show that the GP approach outperforms standard bagging
                 of decision trees, obtaining significantly higher
                 ensemble accuracy over 25 UCI datasets. This superior
                 performance stems from base classifiers having both
                 higher average accuracy and more diversity. Implicitly
                 introducing diversity using GP thus works very well,
                 since evolved base classifiers tend to be highly
                 accurate and diverse.",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "CEC 2009 - A joint meeting of the IEEE, the EPS and
                 the IET. IEEE Catalog Number: CFP09ICE-CDR",
}

Genetic Programming entries for Ulf Johansson Cecilia Sonstrod Tuve Lofstrom Rikard Konig

Citations