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.
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Saitta, L. (2006). Hypothesis Diversity in Ensemble Classification. In: Esposito, F., Raś, Z.W., Malerba, D., Semeraro, G. (eds) Foundations of Intelligent Systems. ISMIS 2006. Lecture Notes in Computer Science(), vol 4203. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11875604_73
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DOI: https://doi.org/10.1007/11875604_73
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45764-0
Online ISBN: 978-3-540-45766-4
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