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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 (2017) Error sensitivity analysis of Delta divergence - A novel measure for classifier incongruence detection Pattern Recognition, 77. pp. 30-44.

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Abstract

The state of classfier 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
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
NameEmailORCID
Kittler, JosefJ.Kittler@surrey.ac.uk
Zor, Cemrec.zor@surrey.ac.uk
Kaloskampis, Ioannis
Hicks, Yulia
Wang, WenwuW.Wang@surrey.ac.uk
Date : 5 December 2017
Funders : EPSRC
Identification Number : 10.1016/j.patcog.2017.11.031
Copyright Disclaimer : © 2017 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license. (http://creativecommons.org/licenses/by/4.0/)
Uncontrolled Keywords : Anomaly detection; Classifier decision incongruence; Bayesian surprise
Depositing User : Clive Harris
Date Deposited : 08 Dec 2017 10:40
Last Modified : 14 Mar 2018 15:59
URI: http://epubs.surrey.ac.uk/id/eprint/845181

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