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. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Saitta, L. (2006). Hypothesis diversity in ensemble classification. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4203 LNAI, pp. 662–670). Springer Verlag. https://doi.org/10.1007/11875604_73
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