Smooth Receiver Operating Characteristics (smROC) Curves

  • William Klement
  • Peter Flach
  • Nathalie Japkowicz
  • Stan Matwin
Conference paper

DOI: 10.1007/978-3-642-23783-6_13

Part of the Lecture Notes in Computer Science book series (LNCS, volume 6912)
Cite this paper as:
Klement W., Flach P., Japkowicz N., Matwin S. (2011) Smooth Receiver Operating Characteristics (smROC) Curves. In: Gunopulos D., Hofmann T., Malerba D., Vazirgiannis M. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2011. Lecture Notes in Computer Science, vol 6912. Springer, Berlin, Heidelberg

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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • William Klement
    • 1
  • Peter Flach
    • 2
  • Nathalie Japkowicz
    • 1
  • Stan Matwin
    • 1
    • 3
  1. 1.School of Electrical Engineering and Computer ScienceUniversity of OttawaCanada
  2. 2.Computer ScienceBristol UniversityUnited Kingdom
  3. 3.Institute of Computer SciencePolish Academy of SciencesWarsawPoland

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