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Abstract: Using an ensemble of classifiers, instead of a single classifier, can lead to improved generalization. The gains obtained by combining however, are often affected more by the selection of what is presented to the combiner, than by the actual combining method that is chosen. In this paper we focus on data selection and classifier training methods, in order to "prepare" classifiers for combining. We review a combining framework for classification problems that quantifies the need for... (Update)
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BibTeX entry: (Update)
Tumer, K. and Ghosh J., "Error Correlation and Error Reduction in Ensemble Classifiers," Connection Science, Special issue on combining artificial neural networks: ensemble approaches, volume 8, numbers 3 & 4, pp 385-404, December 1996. http://citeseer.ist.psu.edu/tumer96error.html More
@article{ tumer96error,
author = "Kagan Tumer and Joydeep Ghosh",
title = "Error Correlation and Error Reduction in Ensemble Classifiers",
journal = "Connection Science",
volume = "8",
number = "3-4",
pages = "385--403",
year = "1996",
url = "citeseer.ist.psu.edu/tumer96error.html" }
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