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Finding Consensus Bayesian Network Structures
Linköping University, Department of Computer and Information Science, Database and information techniques. Linköping University, The Institute of Technology. (ADIT)
2011 (English)In: The journal of artificial intelligence research, ISSN 1076-9757, Vol. 42, 661-687 p.Article in journal (Refereed) Published
Abstract [en]

Suppose that multiple experts (or learning algorithms) provide us with alternative Bayesian network (BN) structures over a domain, and that we are interested in combining them into a single consensus BN structure. Specifically, we are interested in that the consensus BN structure only represents independences all the given BN structures agree upon and that it has as few parameters associated as possible. In this paper, we prove that there may exist several non-equivalent consensus BN structures and that finding one of them is NP-hard. Thus, we decide to resort to heuristics to find an approximated consensus BN structure. In this paper, we consider the heuristic proposed by Matzkevich and Abramson, which builds upon two algorithms, called Methods A and B, for efficiently deriving the minimal directed independence map of a BN structure relative to a given node ordering. Methods A and B are claimed to be correct although no proof is provided (a proof is just sketched). In this paper, we show that Methods A and B are not correct and propose a correction of them.

Place, publisher, year, edition, pages
2011. Vol. 42, 661-687 p.
National Category
Computer Systems
URN: urn:nbn:se:liu:diva-74539DOI: 10.1613/jair.3427OAI: diva2:487619
Available from: 2012-01-31 Created: 2012-01-31 Last updated: 2014-01-17

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Peña, Jose M.
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