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. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Dang, T. H., Marsala, C., Bouchon-Meunier, B., & Boucher, A. (2006). Discrimination-based criteria for the evaluation of classifiers. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4027 LNAI, pp. 552–563). Springer Verlag. https://doi.org/10.1007/11766254_47
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