Learning Structured Bayesian Networks: Combining Abstraction Hierarchies and Tree-Structured Conditional Probability Tables
I will describe our primary contribution, the Tree-Abstraction-Based Search (TABS) algorithm, which learns a data distribution by inducing the graph structure and parameters of a Bayesian network from training data. TABS combines tree structure and attribute-value hierarchies to compactly represent conditional probability tables. In order to construct the attribute-value hierarchies, we investigate two data-driven techniques: a global clustering method, which uses all of the training data to build the attribute-value hierarchies, and can be performed as a preprocessing step; and a local clustering method, which uses only the local network structure to learn attribute-value hierarchies. Empirical results in several benchmark domains show that (1) combining tree structure and attribute-value hierarchies improves the accuracy of generalization, while providing a significant reduction in the number of parameters in the learned networks, and (2) data-derived hierarchies perform as well or better than expert-provided hierarchies.
BIOGRAPHY Dr. Marie desJardins is an associate professor in the Department of Computer Science and Electrical Engineering at the University of Maryland, Baltimore County. Her research is in artificial intelligence, focusing on the areas of machine learning, multi-agent systems, planning, interactive AI techniques, information management, reasoning with uncertainty, and decision theory.
Dr. desJardins can be contacted at the Dept. of Computer Science and Electrical Engineering, University of Maryland Baltimore County, 1000 Hilltop Circle, Baltimore MD 21250, email@example.com,(410) 455-3967.