2016 International Conference on Frontiers of Information Technology (FIT) (2016)
Dec. 19, 2016 to Dec. 21, 2016
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/FIT.2016.024
In this article we investigate the effect of discretization Methods on the Performance of Associative Classifiers. Most of the classification approaches work on the dicretized databases. There are various approaches exploited for the discretizion of the database to compare the performance of the classifiers. The selection of the discretization method greatly influences the classification performance of the classification method. We compare the performance of associative classifier namely CBA on the selective discretizing methods i.e. 1R-D, Ameva-D, Bayesian-D, CACC-D, CADD-D, DIBD-D, ClusterAnalysis-D, ChiMerge-D and Chi2-D in terms of accuracy. The main object of this study is to investigate the impact of discretizing method on the performance of the Associative Classifier by keeping constant other experimental parameters. Our experimental results show that the performance of the Associative Classifier significantly varies with the change of data discretization method. So the accuracy rate of the classifier is highly dependent on the selection of the discretizaing method. For this comparative performance study we use the implementation of these methods in KEEL data mining tool on public datasets.
Data mining, Classification algorithms, Bayes methods, Algorithm design and analysis, Databases, Knowledge discovery, Computer science,Data Mining, Associative Classification, Discetization Methods, KEEL
Zulfiqar Ali, Waseem Shahzad, "Comparative Study of Discretization Methods on the Performance of Associative Classifiers", 2016 International Conference on Frontiers of Information Technology (FIT), vol. 00, no. , pp. 87-92, 2016, doi:10.1109/FIT.2016.024