Performance Evaluation of Associative Classifiers in Perspective of Discretization Methods

Discretization is the process of converting numerical values into categorical values. Contemporary literature study reveals that there are many techniques available for numerical data discretization. The performance of classification method is dependent on the exploitation of the data discretizing method. In this article, we investigate the effect of discretization methods on the performance of associative classifiers. Most of the classification approaches work on the discretized databases. There are various approaches exploited for the discretization 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 classifiers namely CBA and CBA2 on the selective discretizing methods i.e. 1R Discretizer (1R-D), Ameva Discretizer (Ameva-D), Bayesian Discretizer (Bayesian-D), Discretization algorithm based on Class-Attribute Contingency Coefficient (CACC-D), Class-Attribute Dependent Discretizer (CADD-D), Distribution-Index-Based Discretizer (DIBD-D), Cluster Analysis (ClusterAnalysis-D), Chi-Merge Discretizer (ChiMerge-D) and Chi2 Discretizer (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 discretization method. For this comparative performance study, we use the implementation of these methods in KEEL data mining tool on public datasets