Evidence and scenario sensitivities in naive Bayesian classifiers

Empirical evidence shows that naive Bayesian classifiers perform quite well compared to more sophisticated classifiers, even in view of inaccuracies in their parameters. In this paper, we study the effects of such parameter inaccuracies by investigating the sensitivity functions of a naive Bayesian...

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Bibliographic Details
Published in:International journal of approximate reasoning 2008-10, Vol.49 (2), p.398-416
Main Authors: Renooij, Silja, van der Gaag, Linda C.
Format: Article
Language:English
Subjects:
Naive Bayesian classifiers
Robustness
Sensitivity
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Summary:Empirical evidence shows that naive Bayesian classifiers perform quite well compared to more sophisticated classifiers, even in view of inaccuracies in their parameters. In this paper, we study the effects of such parameter inaccuracies by investigating the sensitivity functions of a naive Bayesian network. We show that, as a consequence of the network’s independence properties, these sensitivity functions are highly constrained. We further investigate whether the patterns of sensitivity that follow from these functions support the observed robustness of naive Bayesian classifiers. In addition to standard sensitivities given available evidence, we also study the effect of parameter inaccuracies in view of scenarios of additional evidence. We show that standard sensitivity functions suffice to describe such scenario sensitivities.
ISSN:0888-613X
1873-4731
DOI:10.1016/j.ijar.2008.02.008