Luma Omar
Using theoretical ROC curves for analysing machine learning binary classifiers
Omar, Luma; Ivrissimtzis, Ioannis
Abstract
Most binary classifiers work by processing the input to produce a scalar response and comparing it to a threshold value. The various measures of classifier performance assume, explicitly or implicitly, probability distributions Ps and Pn of the response belonging to either class, probability distributions for the cost of each type of misclassification, and compute a performance score from the expected cost. In machine learning, classifier responses are obtained experimentally and performance scores are computed directly from them, without any assumptions on Ps and Pn. Here, we argue that the omitted step of estimating theoretical distributions for Ps and Pn can be useful. In a biometric security example, we fit beta distributions to the responses of two classifiers, one based on logistic regression and one on ANNs, and use them to establish a categorisation into a small number of classes with different extremal behaviours at the ends of the ROC curves.
Citation
Omar, L., & Ivrissimtzis, I. (2019). Using theoretical ROC curves for analysing machine learning binary classifiers. Pattern Recognition Letters, 128, 447-451. https://doi.org/10.1016/j.patrec.2019.10.004
Journal Article Type | Article |
---|---|
Acceptance Date | Oct 3, 2019 |
Online Publication Date | Oct 4, 2019 |
Publication Date | Dec 31, 2019 |
Deposit Date | Oct 22, 2019 |
Publicly Available Date | Oct 4, 2020 |
Journal | Pattern Recognition Letters |
Print ISSN | 0167-8655 |
Electronic ISSN | 1872-7344 |
Publisher | Elsevier |
Peer Reviewed | Peer Reviewed |
Volume | 128 |
Pages | 447-451 |
DOI | https://doi.org/10.1016/j.patrec.2019.10.004 |
Public URL | https://durham-repository.worktribe.com/output/1281994 |
Related Public URLs | https://arxiv.org/pdf/1909.09816.pdf |
Files
Accepted Journal Article
(584 Kb)
PDF
Publisher Licence URL
http://creativecommons.org/licenses/by-nc-nd/4.0/
Copyright Statement
© 2019 This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
You might also like
Anomaly Detection with Transformers in Face Anti-spoofing
(2023)
Presentation / Conference Contribution
Race Bias Analysis of Bona Fide Errors in Face Anti-spoofing
(2023)
Presentation / Conference Contribution
Exploring the Potential of Immersive Virtual Environments for Learning American Sign Language
(2023)
Presentation / Conference Contribution
Developing and Evaluating a Novel Gamified Virtual Learning Environment for ASL
(2023)
Presentation / Conference Contribution
Bivariate non-uniform subdivision schemes based on L-systems
(2023)
Journal Article
Downloadable Citations
About Durham Research Online (DRO)
Administrator e-mail: dro.admin@durham.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2024
Advanced Search