Affiliations: [a] Centro de Aplicaciones de Tecnologías de Avanzada, Havana, Cuba | [b] Instituto Nacional de Astrofísica Óptica y Electrónica, Puebla, México
Correspondence:
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Corresponding author: R. Hernández-León, Centro de Aplicaciones de Tecnologías de Avanzada, 7a # 21406, Siboney, Playa, CP: 12200, Havana, Cuba. E-mail: [email protected].
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.
Keywords: Data mining, supervised classification, class association rules, rule ordering strategies
