PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Ensemble Learning for Free with Evolutionary Algorithms ?
Christian Gagné, Michele Sebag, Marc Schoenauer and Marco Tomassini
In: Proc. GECCO'07 (2007) ACM Press , pp. 1782-1789.

Abstract

Evolutionary Learning (EL) proceeds by evolving a population of classifiers, and it most often (with some notable exceptions) returns the single best-of-run classifier. 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. 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.

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EPrint Type:Book Section
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:3168
Deposited By:Marc Schoenauer
Deposited On:03 January 2008