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Classification by bagged consistent itemset rules

Associative classifiers that utilize association rules have been widely studied. It has been shown that associative classifiers often outperform traditional classifiers. Associative classifiers usually find only rules with high support values, because reducing the minimum support to be satisfied increases computational cost. However, rules with low support but high confidence may contribute to classification. We have proposed an approach to build a classifier composed of almost all consistent (100% confident) rules. The proposed classifier was extended by introducing item reduction and bagging in order to relax the constraint of consistency, which resulted in slightly increased performance for 26 datasets from the UCI machine learning repository.