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Associative classifiers for predictive analytics: Comparative performance study

: Vyas, R.; Sharma, L.K.; Vyas, O.P.; Scheider, S.


Al-Dabass, D.:
Second UKSim European Symposium on Computer Modeling and Simulation, EMS '08 : Liverpool, 8 - 10 Sept. 2008
Piscataway: IEEE, 2008
ISBN: 978-0-7695-3325-4
ISBN: 978-1-4244-3391-9
European Modelling Symposium (EMS) <2, 2008, Liverpool>
European Symposium on Computer Modeling and Simulation (UKSim) <2, 2008, Liverpool>
Fraunhofer IAIS ()

A new predictive modelling approach known as associative classification, integrating association mining and classification into single system is being discussed as a better alternative for predictive analytics. Our paper investigates the performance issues of significant associative classifiers likes CMAR and CPAR. Performance comparisons observe that CPAR achieves improved performance as compared to CMAR. We have proposed the modification in these approaches by incorporating temporal dimension. The new approach was compared with their non-temporal counterparts and the results were analyzed for classifier accuracy and execution time. The study concludes that temporal CPAR achieves better performance than temporal CBA and temporal CMAR. The three temporal associative classifiers (TACs) were compared on ten different datasets for classifier accuracy and significant conclusion was drawn as temporal associative classifiers performed better than their non-temporal counterpar ts, while temporal CPAR being the best among the three TACs.