Explicitly representing expected cost: an alternative to ROC representation
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
ROC confidence bands: an empirical evaluation
ICML '05 Proceedings of the 22nd international conference on Machine learning
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Cost curves have recently been introduced as an alternative or complement to ROC curves in order to visualize binary classifiers performance. Of importance to both cost and ROC curves is the computation of confidence intervals along with the curves themselves so that the reliability of a classifier's performance can be assessed. Computing confidence intervals for the difference in performance between two classifiers allows the determination of whether one classifier performs significantly better than another. A simple procedure to obtain confidence intervals for costs or the difference between two costs, under various operating conditions, is to perform bootstrap resampling of the test set. In this paper, we derive exact bootstrap distributions for these values and use these dstributions to obtain confidence intervals, under various operating conditions. Performances of these confidence intervals are measured in terms of coverage accuracies. Simulations show excellent results.