Stochastic Constraint Propagation for Mining Probabilistic Networks
| Autor: | Latour, A.L.D., Babaki, B., Nijssen, S., Beuls, K., Bogaerts, B., Bontempi, G., Geurts, P., Harley, N., Lebichot, B., Lenaerts, T., Louppe, G., Eecke, P. van |
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| Přispěvatelé: | UCL - SST/ICTM/INGI - Pôle en ingénierie informatique |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: | |
| Zdroj: | BNAIC/BENELEARN Scopus-Elsevier Proceedings of the 31st Benelux Conference on Artificial Intelligence (BNAIC 2019) and the 28th Belgian Dutch Conference on Machine Learning (Benelearn 2019), 2491, 1137-1145. CEUR-WS |
| Popis: | A number of data mining problems on probabilistic networks can be modeled as Stochastic Constraint Optimization and Satisfaction Problems, i.e., problems that involve objectives or constraints with a stochastic component. Earlier methods for solving these problems used Ordered Binary Decision Diagrams (OBDDs) to represent constraints on probability distributions, which were decomposed into sets of smaller constraints and solved by Constraint Programming (CP) or Mixed Integer Programming (MIP) solvers. For the specific case of monotonic distributions, we propose an alternative method: a new propagator for a global OBDD-based constraint. We show that this propagator is (sub-)linear in the size of the OBDD, and maintains domain consistency. We experimentally evaluate the effectiveness of this global constraint in comparison to existing decomposition-based approaches, and show how this propagator can be used in combination with another data mining specific constraint present in CP systems. As test cases we use problems from the data mining literature. |
| Databáze: | OpenAIRE |
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