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Ensemble Learning for Free with Evolutionary Algorithms ?

Abstract : Evolutionary Learning proceeds by evolving a population of classifiers, from which it generally returns (with some notable exceptions) the single best-of-run classifier as final result. In the meanwhile, Ensemble Learning, one of the most efficient approaches in supervised Machine Learning for the last decade, proceeds by building a population of diverse classifiers. Ensemble Learning with Evolutionary Computation thus receives increasing attention. The Evolutionary Ensemble Lear\-ning (EEL) approach presented in this paper features two contributions. First, a new fitness function, inspired by co-evolution and enforcing the classifier diversity, is presented. Further, a new selection criterion based on the classification margin is proposed. This criterion is used to extract the classifier ensemble from the final population only (Off-line) or incrementally along evolution (On-line). Experiments on a set of benchmark problems show that Off-line outperforms single-hypothesis evolutionary learning and state-of-art Boosting and generates smaller classifier ensembles.
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Contributor : Marc Schoenauer Connect in order to contact the contributor
Submitted on : Monday, April 30, 2007 - 10:35:17 AM
Last modification on : Monday, November 16, 2020 - 8:38:05 AM
Long-term archiving on: : Wednesday, April 7, 2010 - 1:16:50 AM


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  • HAL Id : inria-00144010, version 1
  • ARXIV : 0704.3905



Christian Gagné, Michèle Sebag, Marc Schoenauer, Marco Tomassini. Ensemble Learning for Free with Evolutionary Algorithms ?. GECCO, ACM SIGEVO, Jul 2007, London, United Kingdom. pp.1782-1789. ⟨inria-00144010⟩



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