Comparative Analysis of Naive Bayes and Tree Augmented Naive Bayes Models

Naive Bayes and Tree Augmented Naive Bayes (TAN) are probabilistic graphical models usedfor modeling huge datasets involving lots of uncertainties among its various interdependentfeature sets. Some of the most common applications of these models are image segmentation,medical diagnosis and various other data clustering and data classification applications. Aclassification problem deals with identifying to which category a particular instance belongs to,based on previous knowledge acquired by analysis of various such instances. The instances aredescribed using a set of variables called attributes or features. A Naive Bayes model assumes thatall the attributes of an instance are independent of each other given the class of that instance.This is a very simple representation of the system, but the independence assumptions made inthis model are incorrect and unrealistic. The TAN model improves on the Naive Bayes model byadding one more level of interaction among attributes of the system. In the TAN model, everyattribute is dependent on its class and one other attribute from the feature set. Since this modelincorporates the dependencies among the attributes, it is more realistic than a Naive Bayesmodel. This project analyzes the performance of these two models on various datasets. The TANmodel gives better performance results if there are correlations between the attributes but theperformance is almost the same as that of Naive Bayes model, if there are not enoughcorrelations between the attributes of the system