Evidence and scenario sensitivities in naive Bayesian classifiers

Silja Renooij, Linda C. van der Gaag
2008 International Journal of Approximate Reasoning  
Empirical evidence shows that naive Bayesian classifiers perform quite well compared to more sophisticated network 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 classifier. We demonstrate that, as a consequence of the classifier's independence properties, these sensitivity functions are highly constrained. We investigate whether the various patterns
more » ... f sensitivity that follow from these functions support the observed robustness of naive Bayesian classifiers. In addition to the standard sensitivity given the available evidence, we also study the effect of parameter inaccuracies in view of scenarios of additional evidence. We show that the standard sensitivity functions suffice to describe such scenario sensitivities.
doi:10.1016/j.ijar.2008.02.008 fatcat:pd5vamaocfe6zdtcwbvtmrxyvy