Structure learning via non-parametric factorized joint likelihood function

Abstract

Bayesian Belief Network has inspired the community of machine learning in the domain of structure learning. Numerous scoring functions have been introduced in the structure learning. The performance of these scoring functions usually tends to favor the dense Bayesian Network by using an implicit over-fitting phenomenon. Motivated by this limitation, this study has introduced a novel scoring function which is optimized for producing non complex learnt structures but with the target of higher classification accuracy. The introduced scoring function assumes no external parameter for its fine tuning. It is decomposable and possesses no penalty factor. The introduced scoring function holds its mathematical interpretation within information theory. This scoring function is designed to maximize the discriminant function for given dataset query variables with respect to the class feature and other non class features. The empirical evaluation of the introduced scoring function points out that its classification accuracy is significantly better than other existent scoring functions using greedy search algorithm K2 and hill climber. The outcome of this study illustrates that simplistic structure with higher classification performance is possible by means of exercising the proposed scoring function in the Bayesian Belief structure learning.