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Error sensitivity analysis of Delta divergence - a novel measure for classifier incongruence detection

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. Green open access

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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 > 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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