Classifying Using Specific Rules with High Confidence

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...

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Bibliographic Details
Main Authors: Hernández-León, R, Carrasco-Ochoa, J A, Martínez-Trinidad, J Fco, Hernández-Palancar, J
Format: Conference Proceeding
Language:English
Subjects:
Accuracy
Association Rule Mining
Association rules
Class Association Rules
Classification algorithms
Data Mining
Itemsets
Machine learning
Supervised Classification
Training
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Summary: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.
DOI:10.1109/MICAI.2010.24