Theory Refinement of Bayesian Networks with Hidden Variables (1998)
Research in theory refinement has shown that biasing a learner with initial, approximately correct knowledge produces more accurate results than learning from data alone. While techniques have been developed to revise logical and connectionist representations, little has been done to revise probabilistic representations. Bayesian networks are well-established as a sound formalism for representing and reasoning with probabilistic knowledge, and are widely used. There has been a growing interest in the problem of learning Bayesian networks from data. However, there is no existing technique for learning or revising Bayesian networks with hidden variables (i.e. variables not represented in the data) that has been shown to be efficient, effective, and scalable through evaluation on real data. The few techniques that exist for revising such networks perform a blind search through a large space of revisions, and are therefore computationally expensive. This dissertation presents Banner, a technique for using data to revise a giv en Bayesian network with Noisy-Or and Noisy-And nodes, to improve its classification accuracy. Additionally, the initial netwrk can be derived directly from a logical theory expressed as propositional Horn-clause rules. Banner can revise networks with hidden variables, and add hidden variables when necessary. Unlike previous approaches to this problem, Banner employs mechanisms similar to those used in logical theory refinement techniques for using the data to focus the search for effective modifications to the network. It can also be used to learn networks with hidden variables from data alone. We also introduce Banner-Pr, a technique for revising the parameters of a Bayesian network with Noisy-Or/And nodes, that directly exploits the computational efficiency afforded by these models. Experiments on several real-world learning problems in domains such as molecular biology and intelligent tutoring systems demonstrate that Banner can effectively and efficiently revise networks to significantly improve their accuracies, and thus learn highly accurate classifiers. Comparisons with the Naive Bayes algorithm show that using the theory refinement approach gives Banner a substantial edge over learning from data alone. We also show that Banner-Pr converges faster and produces more accurate classifiers than an existing algorithm for learning the parameters of a network.
PhD Thesis, Department of Computer Sciences, University of Texas at Austin. 139 pages. Also appears as Technical Report AI 98-265, Artificial Intelligence Lab, University of Texas at Austin.

Raymond J. Mooney Faculty mooney [at] cs utexas edu
Sowmya Ramachandran Ph.D. Alumni sowmya [at] shai com