NDLI: Meta-evolutionary ensembles

Content Provider	IEEE Xplore Digital Library
Author	YongSeog Kim Street, W.N. Menczer, F.
Copyright Year	2002
Description	Author affiliation: Dept. of Manage. Sci., Iowa Univ., Iowa City, IA, USA (YongSeog Kim; Street, W.N.; Menczer, F.)
Abstract	Ensemble methods have shown the potential to improve on the performance of individual classifiers as long as the members of the ensemble are sufficiently diverse. In this paper we propose a meta-evolutionary approach in which both individual classifiers and ensembles adapt. Ensembles compete for member classifiers, and are rewarded based on their predictive performance. Individual classifiers also evolve, competing to correctly classify test points, and are given extra rewards for getting difficult points right. In this way we aim to build small-sized optimal ensembles rather than form large-sized ensembles of individually-optimized classifiers. We use neural networks as individual classifiers, and feature selection to promote the diversity among classifiers in the. same ensemble. Experimental results on 17 data sets suggest that our algorithms can generate ensembles that are more effective than single classifiers and traditional ensemble methods. We also use the evolutionary framework to explore the role of ensemble characteristics such as size and diversity in creating accurate ensembles.
Sponsorship	IEEE IEEE Neural Networks Soc. (NNS) Int. Neural Network Soc
Starting Page	2791
Ending Page	2796
File Size	589570
Page Count	6
File Format	PDF
ISBN	0780372786
ISSN	10987576
DOI	10.1109/IJCNN.2002.1007590
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2002-05-12
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Accuracy Cities and towns Predictive models Neural networks Classification tree analysis Decision trees Boosting Testing Machine learning Artificial neural networks
Content Type	Text
Resource Type	Article

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