Exploiting Qualitative Knowledge in the Learning of Conditional Probabilities of Bayesian Networks
This work addresses the challenge of improving interpretability and accuracy in Bayesian network learning for domains with known structure, though it is incremental as it builds on existing algorithms.
The paper tackled the problem of learning conditional probabilities in Bayesian networks with hidden variables, which often leads to high-dimensional search spaces and local optima, by integrating qualitative constraints from background knowledge into APN and EM algorithms. In experiments with synthetic data, this method achieved near-perfect constraint satisfaction and consistently superior accuracy compared to unconstrained learning.
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.