Combining hybrid rule ordering strategies based on netconf and a novel satisfaction mechanism for CAR-based classifiers

Abstract

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.