Recent research in machine learning has shown that it incorporates a small amount of dependencies among attributes involved in a database and represents them graphically in the form of a network, known as Tree Augmented Naive Bayes (TAN). This is competitive with the state-of-the-art classifiers such as the C4.5 decision tree and naive Bayes classifier. This thesis explores approaches for constructing Bayesian networks, based on the theory of naive Bayes classifier and existing dependencies among attributes.
One of the proposed approaches for finding the dependencies, is to apply naive Bayes classifier between the attributes itself, then using the attributes with dependencies to construct Bayesian network. This proposed approach is called Bayesian Network with Naive Dependence (BNND). The prediction performance from this network is competitively better than the C4.5 decision tree and naive Bayes classifier. However, if there are too many dependencies found, then the prediction performance of the network is significantly reduced. Therefore, this paper also explores the uses of feature selection to select well-related attributes, in order to reduce the chance of over-fitting in Bayesian network and to increase the computational time.
Author:- Newton Lim-Tung Cheung
Source:-University of Queensland