Hypothesis diversity in ensemble classification

Authors:
Lorenza Saitta
Affiliations:
Dipartimento di Informatica, Università del Piemonte Orientale, Alessandria, Italy
Venue:
ISMIS'06 Proceedings of the 16th international conference on Foundations of Intelligent Systems
Year:
2006

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Abstract

The paper discusses the issue of hypothesis diversity in ensemble classifiers. The measures of diversity previously proposed in the literature are analyzed inside a unifying framework based on Monte Carlo stochastic algorithms. The paper shows that no measure is useful to predict ensemble performance, because all of them have only a very loose relation with the expected accuracy of the classifier.