Reasoning about bayesian network classifiers

Authors:
Hei Chan;Adnan Darwiche
Affiliations:
Computer Science Department, University of California, Los Angeles, Los Angeles, CA;Computer Science Department, University of California, Los Angeles, Los Angeles, CA
Venue:
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Year:
2002

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Abstract

Bayesian network classifiers are used in many fields, and one common class of classifiers are naive Bayes classifiers. In this paper, we introduce an approach for reasoning about Bayesian network classifiers in which we explicitly convert them into Ordered Decision Diagrams (ODDS), which are then used to reason about the properties of these classifiers. Specifically, we present an algorithm for converting any naive Bayes classifier into an ODD, and we show theoretically and experimentally that this algorithm can give us an ODD that is tractable in size cvcn given an intractable number of instances. Since ODDS are tractable representations of classifiers, our algorithm allows us to efficiently test the equivalence of two naive Bayes classifiers and characterize discrepancies between them. We also show a number of additional results including a count of distinct classifiers that can be induced by changing some CPT in a naive Bayes classifier, and the range of allowable changes to a CPT which keeps the current classifier unchanged.