Genetic approach for optimizing ensemble of classifiers. Ordoñez, F. J., Ledezma, A., & Sanchis, A. In Wilson, D. & Lane, H. C., editors, Proceedings of the 21th International Florida Artificial Intelligence Research Society Conference - FLAIRS 2008, pages 89–94, Coconut Grove, Florida, USA, May, 2008. AAAI Press.
abstract   bibtex   
An ensemble of classifiers is a set of classifiers whose predictions are combined in some way to classify new instances. Early research has shown that, in general, an ensemble of classifiers is more accurate than any of the single classifiers in the ensemble. Usually the gains obtained by combining different classifiers are more affected by the chosen classifiers than by the used combination. It is common in the research on this topic to select by hand the right combination of classifiers and the method to combine them, but the approach presented in this work uses genetic algorithms for selecting the classifiers and the combination method to use. Our approach, GA-Ensemble, is inspired by a previous work, called GA-Stacking. GA-Stacking is a method that uses genetic algorithms to find domain-specific Stacking configurations. The main goal of this work is to improve the efficiency of GA-Stacking and to compare GA-Ensemble with current ensemble building techniques. Preliminary results have show that the approach finds ensembles of classifiers whose performance is as good as the best techniques, without having to set up manually the classifiers and the ensemble method.
@INPROCEEDINGS{gaensemble-flairs08,
  author = {Francisco Javier Ordoñez and Agapito Ledezma and Araceli Sanchis},
  title = {Genetic approach for optimizing ensemble of classifiers},
  booktitle = {Proceedings of the 21th International Florida Artificial Intelligence
	Research Society Conference - FLAIRS 2008},
  year = {2008},
  editor = {David Wilson and H. Chad Lane},
  pages = {89--94},
  address = {Coconut Grove, Florida, USA},
  month = {May},
  publisher = {AAAI Press},
  abstract = {An ensemble of classifiers is a set of classifiers whose predictions
	are combined in some way to classify new instances. Early research
	has shown that, in general, an ensemble of classifiers is more accurate
	than any of the single classifiers in the ensemble. Usually the gains
	obtained by combining different classifiers are more affected by
	the chosen classifiers than by the used combination. It is common
	in the research on this topic to select by hand the right combination
	of classifiers and the method to combine them, but the approach presented
	in this work uses genetic algorithms for selecting the classifiers
	and the combination method to use. Our approach, GA-Ensemble, is
	inspired by a previous work, called GA-Stacking. GA-Stacking is a
	method that uses genetic algorithms to find domain-specific Stacking
	configurations. The main goal of this work is to improve the efficiency
	of GA-Stacking and to compare GA-Ensemble with current ensemble building
	techniques. Preliminary results have show that the approach finds
	ensembles of classifiers whose performance is as good as the best
	techniques, without having to set up manually the classifiers and
	the ensemble method.},
  bib2html_pubtype = {Refereed Conference},
  bib2html_rescat = {Ensemble of Classifiers},
  days = {15--17},
  isbn = {978-1-57735-365-2},
  owner = {ledezma},
  timestamp = {2008.07.22}
}

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