Abstract
An ensemble of classifiers (EoC) has been shown to be effective in improving classifier performance. To optimize EoC, the ensemble selection is one of the most imporatant issues. Dynamic scheme urges the use of different ensembles for different samples, but it has been shown that dynamic selection does not give better performance than static selection. We propose a dynamic selection scheme which explores the property of the oracle concept. The result suggests that the proposed scheme is apparently better than the selection based on popular majority voting error. © Springer-Verlag Berlin Heidelberg 2007.
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CITATION STYLE
Ko, A. H. R., Sabourin, R., & De Britto, A. S. (2007). A new dynamic ensemble selection method for numeral recognition. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4472 LNCS, pp. 431–439). Springer Verlag. https://doi.org/10.1007/978-3-540-72523-7_43
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