In this paper we extend previous results providing a theoretical analysis of a new Monte Carlo ensemble classifier. The framework allows us to characterize the conditions under which the ensemble approach can be expected to outperform the single hypothesis classifier. Moreover, we provide a closed form expression for the distribution of the true ensemble accuracy, as well as of its mean and variance. We then exploit this result in order to analyze the expected error behavior in a particularly interesting case.

A monte carlo analysis of ensemble classification

ESPOSITO, Roberto;
2004-01-01

Abstract

In this paper we extend previous results providing a theoretical analysis of a new Monte Carlo ensemble classifier. The framework allows us to characterize the conditions under which the ensemble approach can be expected to outperform the single hypothesis classifier. Moreover, we provide a closed form expression for the distribution of the true ensemble accuracy, as well as of its mean and variance. We then exploit this result in order to analyze the expected error behavior in a particularly interesting case.
Twenty-first International Conference on Machine Learning (ICML 2004)
Banff, Alberta, Canada
6-7-2004
Proceedings of the twenty-first international conference on Machine learning
ACM Press
Vol.
34
41
9781581138283
http://portal.acm.org/citation.cfm?id=1015386
R. ESPOSITO; L. SAITTA
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/28787
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