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In Associative Classification, building a classifier based on Class Association Rules (CARs) consists in finding an ordered CAR list by applying a rule ordering strategy, and selecting a satisfaction mechanism to determine the class of unseen transactions. In this paper, we introduce four novel hybrid rule ordering strategies; the first three combine the Netconf measure with different Support-Confidence based rule ordering strategies. The fourth strategy combines the Netconf measure with a rule ordering strategy based on the CAR's size. Additionally, we combine the proposed strategies with a novel "Dynamic K" satisfaction mechanism. Experiments over several datasets show that the proposed rule ordering strategies jointly with the "Dynamic K" satisfaction mechanism allow improving the performance of CAR-based classifiers. [ABSTRACT FROM AUTHOR]

In this paper, an accurate classifier based on Class Association Rules (CARs), called CAR-NF, is proposed. CAR-NF introduces a new strategy for computing CARs, using the Netconf as measure of interest, that allows to prune the CAR search space for building specific rules with high Netconf. Moreover, we propose and prove a proposition that supports the use of a Netconf threshold value equal to 0.5 for mining the CARs. Additionally, a new way for ordering the set of CARs based on their rule sizes and Netconf values is introduced in CAR-NF. The ordering strategy together with the "Best K rules" satisfaction mechanism allows CAR-NF to have better accuracy than CBA, CMAR, CPAR, TFPC and HARMONY classifiers, the best classifiers based on CARs reported in the literature. [ABSTRACT FROM AUTHOR]