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Stopping Criterion for Boosting-Based Data Reduction Techniques: from Binary to Multiclass Problem.

, , and . J. Mach. Learn. Res., (2002)

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Contrôle du risque multiple pour la sélection de règles d'association significatives., , and . EGC, volume RNTI-E-2 of Revue des Nouvelles Technologies de l'Information, page 305-316. Cépaduès-Éditions, (2004)What to expect from a set of itemsets?, , and . Inf. Sci., (2022)Outlier Handling in the Neighbourhood-Based Learning of a Continuous Class., , and . Discovery Science, volume 3245 of Lecture Notes in Computer Science, page 314-321. Springer, (2004)All-Monotony: A Generalization of the All-Confidence Antimonotony., , , and . ICMLA, page 759-764. IEEE Computer Society, (2009)Constrained independence for detecting interesting patterns., , , and . DSAA, page 1-10. IEEE, (2015)A Clustering of Interestingness Measures., , and . Discovery Science, volume 3245 of Lecture Notes in Computer Science, page 290-297. Springer, (2004)Boosting Neighborhood-Based Classifiers., , and . ICML, page 505-512. Morgan Kaufmann, (2001)Association Rule Interestingness Measures: Experimental and Theoretical Studies., , , and . Quality Measures in Data Mining, volume 43 of Studies in Computational Intelligence, Springer, (2007)ClusPath: a temporal-driven clustering to infer typical evolution paths., , , and . Data Min. Knowl. Discov., 30 (5): 1324-1349 (2016)Mesurer l'intérêt des règles d'association., , , , , and . EGC (Ateliers), volume E-5 of RNTI, page 421-426. Cépaduès-Éditions, (2005)