Publications

Ensemble Learning for Free with Evolutionary Algorithms?

Christian Gagné, Michèle Sebag, Marc Schoenauer and Marco Tomassini

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 Learning (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-EEL) or incrementally along evolution (On-EEL). Experiments on a set of benchmark problems show that Off-EEL outperforms single-hypothesis evolutionary learning and state-of-art Boosting and generates smaller classifier ensembles.

Bibtex:

@inproceedings{Gagné691,
author    = { Christian Gagné and Michèle Sebag and Marc Schoenauer and Marco Tomassini },
title     = { Ensemble Learning for Free with Evolutionary Algorithms? },
booktitle = { Proc. of the Genetic and Evolutionary Computation Conference (GECCO 2007) },
pages     = { 1782-1789 },
year      = { 2007 },
month     = { July 7-11 },
location  = { London, UK }
}

Dernière modification: 2007/08/13 par cgagne

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