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

@inproceedings{Delany2005GeneratingEO,
  title={Generating Estimates of Classification Confidence for a Case-Based Spam Filter},
  author={Sarah Jane Delany and Padraig Cunningham and D{\'o}nal Doyle and Anton Zamolotskikh},
  booktitle={ICCBR},
  year={2005}
}
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… CONTINUE READING

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