Kittler, J;
Zor, C;
Kaloskampis, I;
Hicks, Y;
Wang, W;
(2018)
Error sensitivity analysis of Delta divergence - a novel measure for classifier incongruence detection.
Pattern Recognition
, 77
pp. 30-44.
10.1016/j.patcog.2017.11.031.
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Abstract
The state of classifier incongruence in decision making systems incorporating multiple classifiers is often an indicator of anomaly caused by an unexpected observation or an unusual situation. Its assessment is important as one of the key mechanisms for domain anomaly detection. In this paper, we investigate the sensitivity of Delta divergence, a novel measure of classifier incongruence, to estimation errors. Statistical properties of Delta divergence are analysed both theoretically and experimentally. The results of the analysis provide guidelines on the selection of threshold for classifier incongruence detection based on this measure.
| Type: | Article |
|---|---|
| Title: | Error sensitivity analysis of Delta divergence - a novel measure for classifier incongruence detection |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.patcog.2017.11.031 |
| Publisher version: | https://doi.org/10.1016/j.patcog.2017.11.031 |
| Language: | English |
| Additional information: | © 2017 The Authors. This is an open access article under the CC BY license. (http://creativecommons.org/licenses/by/4.0/) |
| Keywords: | Anomaly detection, Classifier decision incongruence, Bayesian surprise |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10067204 |
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