Title :
Classifying Using Specific Rules with High Confidence
Author :
Hernández-León, R. ; Carrasco-Ochoa, J.A. ; Martínez-Trinidad, J. Fco ; Hernández-Palancar, J.
Author_Institution :
Comput. Sci. Dept., Nat. Inst. of Astrophys., Opt. & Electron., Puebla, Mexico
Abstract :
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.
Keywords :
data mining; pattern classification; class association rule mining; confidence threshold value; specific rule classification; Association Rule Mining; Class Association Rules; Data Mining; Supervised Classification;
Conference_Titel :
Artificial Intelligence (MICAI), 2010 Ninth Mexican International Conference on
Conference_Location :
Pachuca
Print_ISBN :
978-0-7695-4284-3
DOI :
10.1109/MICAI.2010.24
