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Rule based classifiers that use the presence and absence of key sub-stringsto make classification decisions have a natural mechanism for quantifying theuncertainty of their precision. For a binary classifier, the key insight is totreat partitions of the sub-string set induced by the documents as Bernoullirandom variables. The mean value of each random variable is an estimate of theclassifier's precision when presented with a document inducing that partition.These means can be compared, using standard statistical tests, to a desired orexpected classifier precision. A set of binary classifiers can be combined intoa single, multi-label classifier by an application of the Dempster-Shafertheory of evidence. The utility of this approach is demonstrated with abenchmark problem.

Quantifying the Uncertainty of Precision Estimates for Rule based Text Classifiers