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. Since this CAR list will be used to build a classifier, it is important to develop a good rule ordering strategy. In this paper, we introduce four novel hybrid rule ordering strategies; the first three combine the Netconf measure with Support-Confidence based rule ordering strategies. The fourth strategy, called Hybrid Specific Rules/Netconf (SR/NF), combines the Netconf measure with a rule ordering strategy based on the CAR’s size. The experiments show that the proposed strategies obtain better classification accuracy than the best ordering strategies reported in the literature.
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Hernández-León, R., Hernández-Palancar, J., Carrasco-Ochoa, J.A., Martínez-Trinidad, J.F. (2014). Studying Netconf in Hybrid Rule Ordering Strategies for Associative Classification. In: Martínez-Trinidad, J.F., Carrasco-Ochoa, J.A., Olvera-Lopez, J.A., Salas-Rodríguez, J., Suen, C.Y. (eds) Pattern Recognition. MCPR 2014. Lecture Notes in Computer Science, vol 8495. Springer, Cham. https://doi.org/10.1007/978-3-319-07491-7_6
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DOI: https://doi.org/10.1007/978-3-319-07491-7_6
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