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Bayesian Network Refinement Via Machine Learning Approach
March 1998 (vol. 20 no. 3)
pp. 240-251

Abstract—A new approach to refining Bayesian network structures from new data is developed. Most previous work has only considered the refinement of the network's conditional probability parameters and has not addressed the issue of refining the network's structure. We tackle this problem by a machine learning approach based on a formalism known as the Minimum Description Length (MDL) principle. The MDL principle is well suited to this task since it can perform tradeoffs between the accuracy, simplicity, and closeness to the existent structure. Another salient feature of this refinement approach is the capability of refining a network structure using partially specified data. Moreover, a localization scheme is developed for efficient computation of the description lengths since direct evaluation involves exponential time resources.

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Index Terms:
Knowledge base refinement, uncertainty reasoning, Bayesian networks, machine learning, data mining.
Citation:
Wai Lam, "Bayesian Network Refinement Via Machine Learning Approach," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20, no. 3, pp. 240-251, March 1998, doi:10.1109/34.667882
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