Generating Estimates of Classification Confidence for a Case-Based Spam Filter

Abstract

Producing estimates of classification confidence is surprisingly difficult. One might expect that classifiers that can produce numeric classification scores (e.g. k-Nearest Neighbour or Naive Bayes) could readily produce confidence estimates based on thresholds. In fact, this proves not to be the case, probably because these are not probabilistic classifiers in the strict sense. The numeric scores coming from k-Nearest Neighbour or Naive Bayes classifiers are not well correlated with classification confidence. In this paper we describe a case-based spam filtering application that would benefit significantly from an ability to attach confidence predictions to positive classifications (i.e. messages classified as spam). We show that `obvious? confidence metrics for a case-based classifier are not effective. We propose an ensemble-like solution that aggregates a collection of confidence metrics and show that this offers an effective solution in this spam filtering domain.