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Using an ensemble of classifiers instead of a single classifier has been demonstrated to improve generalization performance in many difficult problems. However, for this improvement to take place it is necessary to make the classifiers in an ensemble more complementary. In this paper, we highlight the need to reduce the correlation among the component classifiers and investigate one method for correlation reduction: input decimation. We elaborate on input decimation, a method that uses the discriminating features of the inputs to decouple classifiers. By presenting different parts of the feature set to each individual classifier, input decimation generates a diverse pool of classifiers. Experimental results confirm that input decimation combining improves generalization performance.
(unavailable)
@inproceedings{tumer-oza_ijcnn99,
author={K. Tumer and N. C. Oza},
title={Decimated Input Ensembles for Improved Generalization},
booktitle={Proceedings of the International Joint Conference on
Neural Networks},
address={Washington, D.C.},
month = {July},
abstract={Using an ensemble of classifiers instead of a single classifier has been demonstrated to improve generalization performance in many difficult problems. However, for this improvement to take place it is necessary to make the classifiers in an ensemble more complementary. In this paper, we highlight the need to reduce the correlation among the component classifiers and investigate one method for correlation reduction: input decimation. We elaborate on input decimation, a method that uses the discriminating features of the inputs to decouple classifiers. By presenting different parts of the feature set to each individual classifier, input decimation generates a diverse pool of classifiers. Experimental results confirm that input decimation combining improves generalization performance.},
bib2html_pubtype = {Refereed Conference Papers},
bib2html_rescat = {Classifier Ensembles},
year={1999}
}
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