Uncertainty in Artificial Intelligence
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Critical Remarks on Single Link Search in Learning Belief Networks
Yang Xiang, Michael Wong, N. Cercone
Abstract:
In learning belief networks, the single link lookahead search is widely adopted to reduce the search space. We show that there exists a class of probabilistic domain models which displays a special pattern of dependency. We analyze the behavior of several learning algorithms using different scoring metrics such as the entropy, conditional independence, minimal description length and Bayesian metrics. We demonstrate that single link lookahead search procedures (employed in these algorithms) cannot learn these models correctly. Thus, when the underlying domain model actually belongs to this class, the use of a single link search procedure will result in learning of an incorrect model. This may lead to inference errors when the model is used. Our analysis suggests that if the prior knowledge about a domain does not rule out the possible existence of these models, a multi-link lookahead search or other heuristics should be used for the learning process.
Keywords: Inductive learning, data-mining, Bayesian networks.
Pages: 564-571
PS Link: http://cs.uregina.ca/~yxiang/paper/uai96.ps
PDF Link: /papers/96/p564-xiang.pdf
BibTex:
@INPROCEEDINGS{Xiang96,
AUTHOR = "Yang Xiang and Michael Wong and N. Cercone",
TITLE = "Critical Remarks on Single Link Search in Learning Belief Networks",
BOOKTITLE = "Proceedings of the Twelfth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-96)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1996",
PAGES = "564--571"
}


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