Study of discretization methods in classification

Classification is one of the important tasks in Data Mining or Knowledge Discovery with prolific applications. Satisfactory classification depends on characteristics of the dataset too. Numerical and nominal attributes are commonly occurred in the dataset. However, classification performance may be aided by discretization of numerical attributes. At present, several discretization methods and numerous techniques for implementing classifiers exist. This study has three main objectives. First is to study the effectiveness of discretization of attributes, and second is to compare the efficiency of eight discretization methods. These are ChiMerge, Chi2, Modified Chi2, Extended Chi2, Class-Attribute Interdependence Maximization (CAIM), Class-Attribute Contingency Coefficient (CACC), Autonomous Discretization Algorithm (Ameva), and Minimum Description Length Principle (MDLP). Finally, the study investigates suitability of the eight discretization methods when applied to the five commonly known classifiers, Neural Network, K Nearest Neighbour (K-NN), Naive Bayes, C4.5, and Support Vector machine (SVM).