Classifying Using Specific Rules with High Confidence
Raudel Hernández-León, Jesús Ariel Carrasco-Ochoa, José Fco. Martínez-Trinidad, José Hernández-Palancar · 2010
In this paper, we introduce a new strategy for mining the set of Class Association Rules (CARs), that allows building specific rules with high confidence. Moreover, we introduce two propositions that support the use of a confidence threshold value equal to 0.5. We also propose a new way for ordering the set of CARs based on rule size and confidence values. Our results show a better average classification accuracy than those obtained by the best classifiers based on CARs reported in the literature.