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2006

Optimal ensemble construction via meta-evolutionary ensembles

11 years 1 months ago
Optimal ensemble construction via meta-evolutionary ensembles
In this paper we propose a meta-evolutionary approach to improve on the performance of individual classifiers. In the proposed system, individual classifiers evolve, competing to correctly classify test points, and are given extra rewards for getting difficult points right. Ensembles consisting of multiple classifiers also compete for member classifiers, and are rewarded based on their predictive performance. In this way we aim to build small-sized optimal ensembles rather than form large-sized ensembles of individually-optimized classifiers. Experimental results on 15 data sets suggest that our algorithms can generate ensembles that are more effective than single classifiers and traditional ensemble methods. Key words: Optimal ensemble, evolutionary ensemble, feature selection, neural networks, diversity of ensemble, ensemble size.
YongSeog Kim, W. Nick Street, Filippo Menczer
Added 12 Dec 2010
Updated 12 Dec 2010
Type Journal
Year 2006
Where ESWA
Authors YongSeog Kim, W. Nick Street, Filippo Menczer
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