Cited by 2,276Imbalanced Data Classification TechniquesMachine Learning and Data ClassificationData Mining and Machine Learning Applications

A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification Problems

Open access
Hand, David J., Till, Robert J.·2001·Machine Learning

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1023/a:1010920819831

Cites

22 of 22 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • The time complexity for computing the AUC is with respect to the Hand and Till AUC model and according to Provost and Domingo's AUC model.

    “In addition, the time complexity for computing the AUC is with respect to the Hand and Till AUC model [341] and according to Provost and Domingo’s AUC model [336].”

    From Review of deep learning: concepts, CNN architectures, challenges, applications, future directions (2021) · cites this paper as [341]

    Section 3 (“Multiple class extensions”; page unavailable) describes the Hand–Till method for computing a multiclass AUC measure. The cited paper does not give a time-complexity analysis for computing it, so it supports the identification of the model but not the attributed complexity point. (LLM, conf=0.78)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 3

  1. Provost, F. J., Fawcett, T., & Kohavi, R. (1998). The case against accuracy estimation for comparing classifiers. In Proceedings of the Fifteenth International Conference on Machine Learning (pp. 445-453). Madison, WI: Morgan Kaufmann.
  2. Scott, M. (1999). Parcel: Feature selection in variable cost domains. Doctoral Dissertation, Engineering Depart- ment, Cambridge University, UK.
  3. Taylor, P. C. & Hand, D. J. (1999). Finding superclassifications with acceptable misclassification rates. Journal of Applied Statistics, 26, 579-590.

Cited by

Sorted by citations

Authors
Hand, D. J., & Till, R. J.
Affiliations
Imperial College London (GB)
Venue
Machine Learning
Published
2001-11
Volume
45
Issue
2
Pages
171-186
ISSN
  • 0885-6125 (ISSN-L)
  • 1573-0565
OpenAlex
W1912982817
Cited by
2,276
References
22
Citations by year
2026: 153 · 2025: 148 · 2024: 142 · 2023: 157 · 2022: 174 · 2021: 188 · 2020: 141 · 2019: 134 · 2018: 121 · 2017: 90 · 2016: 87 · 2015: 87 · 2014: 88 · 2013: 81 · 2012: 84
FWCI
16.01
Type
journal article
Language
en
Open access
Locations
Machine Learning (publishedVersion)
Funders
  • Royal Society
  • Engineering and Physical Sciences Research Council
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Imbalanced Data Classification Techniques
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
  • Physical Sciences › Computer Science › Information Systems › Data Mining and Machine Learning Applications
Keywords
area under the ROC curve, Gini coefficient, multi-class classification, pairwise comparison, classification performance measures

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review