Chapter

Machine Learning and Knowledge Discovery in Databases

Volume 6912 of the series Lecture Notes in Computer Science pp 193-208

Smooth Receiver Operating Characteristics (smROC) Curves

  • William KlementAffiliated withSchool of Electrical Engineering and Computer Science, University of Ottawa
  • , Peter FlachAffiliated withComputer Science, Bristol University
  • , Nathalie JapkowiczAffiliated withSchool of Electrical Engineering and Computer Science, University of Ottawa
  • , Stan MatwinAffiliated withSchool of Electrical Engineering and Computer Science, University of OttawaInstitute of Computer Science, Polish Academy of Sciences

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Abstract

Supervised learning algorithms perform common tasks including classification, ranking, scoring, and probability estimation. We investigate how scoring information, often produced by these models, is utilized by an evaluation measure. The ROC curve represents a visualization of the ranking performance of classifiers. However, they ignore the scores which can be quite informative. While this ignored information is less precise than that given by probabilities, it is much more detailed than that conveyed by ranking. This paper presents a novel method to weight the ROC curve by these scores. We call it the Smooth ROC (smROC) curve, and we demonstrate how it can be used to visualize the performance of learning models. We report experimental results to show that the smROC is appropriate for measuring performance similarities and differences between learning models, and is more sensitive to performance characteristics than the standard ROC curve.