Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Using Feature Hierarchies in Bayesian Network Learning
SARA '02 Proceedings of the 4th International Symposium on Abstraction, Reformulation, and Approximation
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
A Bayesian approach to learning Bayesian networks with local structure
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Context-specific independence in Bayesian networks
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Learning Bayesian networks with local structure
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Towards software health management with bayesian networks
Proceedings of the FSE/SDP workshop on Future of software engineering research
Towards an automatic construction of Contextual Attribute-Value Taxonomies
Proceedings of the 27th Annual ACM Symposium on Applied Computing
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Context-specific independence representations, such as tree-structured conditional probability tables (TCPTs), reduce the number of parameters in Bayesian networks by capturing local independence relationships and improve the quality of learned Bayesian networks. We previously presented Abstraction-Based Search (ABS), a technique for using attribute value hierarchies during Bayesian network learning to remove unimportant distinctions within the CPTs. In this paper, we introduce TCPT ABS (TABS), which integrates ABS with TCPT learning. Since expert-provided hierarchies may not be available, we provide a clustering technique for deriving hierarchies from data. We present empirical results for three real-world domains, finding that (1) combining TCPTs and ABS provides a significant increase in the quality of learned Bayesian networks (2) combining TCPTs and ABS provides a dramatic reduction in the number of parameters in the learned networks, and (3) data-derived hierarchies perform as well or better than expert-provided hierarchies.