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2000

Exploiting Qualitative Knowledge in the Learning of Conditional Probabilities of Bayesian Networks

14 years 2 days ago
Exploiting Qualitative Knowledge in the Learning of Conditional Probabilities of Bayesian Networks
Algorithms for learning the conditional probabilities of Bayesian networks with hidden variables typically operate within a high-dimensional search space and yield only locally optimal solutions. One way of limiting the search space and avoiding local optima is to impose qualitative constraints that are based on background knowledge concerning the domain. We present a method for integrating formal statements of qualitative constraints into two learning algorithms, APN and EM. In our experiments with synthetic data, this method yielded networks that satisfied the constraints almost perfectly. The accuracy of the learned networks was consistently superior to that of corresponding networks learned without constraints. The exploitation of qualitative constraints therefore appears to be a promising way to increase both the interpretability and the accuracy of learned Bayesian networks with known structure. If you don't know where you're going, you might wind up someplace else. --...
Frank Wittig, Anthony Jameson
Added 01 Nov 2010
Updated 01 Nov 2010
Type Conference
Year 2000
Where UAI
Authors Frank Wittig, Anthony Jameson
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