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Discrimination-based criteria for the evaluation of classifiers

Abstract : Evaluating the performance of classifiers is a difficult task in machine learning. Many criteria have been proposed and used in such a process. Each criterion measures some facets of classifiers. However, none is good enough for all cases. In this communication, we justify the use of discrimination measures for evaluating classifiers. The justification is mainly based on a hierarchical model for discrimination measures, which was introduced and used in the induction of decision trees.
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Submitted on : Friday, June 24, 2016 - 3:10:09 PM
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Thanh Ha Dang, Christophe Marsala, Bernadette Bouchon-Meunier, Alain Boucher. Discrimination-based criteria for the evaluation of classifiers. 7th International Conference on Flexible Query-Answering Systems, FQAS 2006, Jun 2006, Milan, Italy. pp.552-563, ⟨10.1007/11766254_47⟩. ⟨hal-01337123⟩



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