Abstract
Learning a Bayesian network structure from data is a well-motivated but computationally hard task, especially for problems exhibiting synergic multivariate interactions. In this paper, a novel search method for structure learning of a Bayesian networks from binary data is proposed. The proposed method applies an entropy distillation operation over bounded groups of variables. A bias from the expected increase in randomness signals an underlaying statistical dependence between the inputs. The detected higher-order dependencies are used to connect linked attributes in the Bayesian network in a single step.
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Iclănzan, D. (2012). A Multi-parent Search Operator for Bayesian Network Building. In: Coello, C.A.C., Cutello, V., Deb, K., Forrest, S., Nicosia, G., Pavone, M. (eds) Parallel Problem Solving from Nature - PPSN XII. PPSN 2012. Lecture Notes in Computer Science, vol 7491. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32937-1_25
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DOI: https://doi.org/10.1007/978-3-642-32937-1_25
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