Abstract
In this paper, we present an algorithm for learning the most probable structure of a Bayesian Network from a database of cases. Starting from two previous algorithms, K2 of Cooper and Herskovits, and B of Buntime, we developed a new algorithm that relaxes the assumption of total ordering on the nodes needed by K2 and has less computations than B. To improve our algorithm, we added some heuristics and an interactive process with the user.
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Khalfallah, F., Mellouli, K. (1999). Optimized Algorithm for Learning Bayesian Network from Data. In: Hunter, A., Parsons, S. (eds) Symbolic and Quantitative Approaches to Reasoning and Uncertainty. ECSQARU 1999. Lecture Notes in Computer Science(), vol 1638. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48747-6_21
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DOI: https://doi.org/10.1007/3-540-48747-6_21
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