An Almost Surely Optimal Combined Classification Rule

We propose a data-based procedure for combining a number of individual classifiers in order to construct more effective classification rules. Under some regularity conditions, the resulting combined classifier turns out to be almost surely superior to each of the individual classifiers. Here, superiority means lower misclassification error rate.

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Year of publication:	2002
Authors:	Mojirsheibani, Majid
Published in:	Journal of Multivariate Analysis. - Elsevier, ISSN 0047-259X. - Vol. 81.2002, 1, p. 28-46
Publisher:	Elsevier
Keywords:	Bayes rule misclassification error consistencey Vapnik-Chervonenkis

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Type of publication:	Article
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10005106967