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

Kittler, Josef, Zor, Cemre, Kaloskampis, Ioannis, Hicks, Yulia and Wang, Wenwu 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.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Engineering
Publisher: Elsevier
ISSN: 0031-3203
Date of First Compliant Deposit: 2 January 2018
Date of Acceptance: 30 November 2017
Last Modified: 02 Jan 2018 12:33
URI: http://orca.cf.ac.uk/id/eprint/107772

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