SSV criterion based discretization for naive bayes classifiers

Abstract

Decision tree algorithms deal with continuous variables by finding split points which provide best separation of objects belonging to different classes. Such criteria can also be used to augment methods which require or prefer symbolic data. A tool for continuous data discretization based on the SSV criterion (designed for decision trees) has been constructed. It significantly improves the performance of Naive Bayes Classifier. The combination of the two methods has been tested on 15 datasets from UCI repository and compared with similar approaches. The comparison confirms the robustness of the system.