A Novel Methodology for Constructing Rule-based Naïve Bayesian Classifiers

Classification is an important data mining technique that is used by many applications. Several types of classifiers have been described in the research literature. Example classifiers are decision tree classifiers, rule-based classifiers, and neural networks classifiers. Another popular classification technique is naïve Bayesian classification. Naïve Bayesian classification is a probabilistic classification approach that uses Bayesian Theorem to predict the classes of unclassified records. A drawback of Naïve Bayesian Classification is that every time a new data record is to be classified, the entire dataset needs to be scanned in order to apply a set of equations that perform the classification. Scanning the dataset is normally a very costly step especially if the dataset is very large. To alleviate this problem, a new approach for using naïve Bayesian classification is introduced in this study. In this approach, a set of classification rules is constructed on top of naïve Bayesian classifier. Hence we call this approach Rule-based Naïve Bayesian Classifier (RNBC). In RNBC, the dataset is canned only once, off-line, at the time of building the classification rule set. Subsequent scanning of the dataset, is avoided. Furthermore, this study introduces a simple three-step methodology for constructing the classification rule set.