Letters: On the evolutionary design of heterogeneous Bagging models

Authors:
André L. V. Coelho;Diego S. C. Nascimento
Affiliations:
University of Fortaleza, Graduate Program in Applied Informatics, Av. Washington Soares 1321, 60811-905 Fortaleza, Brazil;University of Fortaleza, Graduate Program in Applied Informatics, Av. Washington Soares 1321, 60811-905 Fortaleza, Brazil
Venue:
Neurocomputing
Year:
2010

Citing 9
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Abstract

Bagging is a popular ensemble algorithm based on the idea of data resampling. In this paper, aiming at increasing the incurred levels of ensemble diversity, we present an evolutionary approach for optimally designing Bagging models composed of heterogeneous components. To assess its potentials, experiments with well-known learning algorithms and classification datasets are discussed whereby the accuracy, generalization and diversity levels achieved with heterogeneous Bagging are matched against those delivered by standard Bagging with homogeneous components.