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Delta divergence: A novel decision cognizant measure of classifier incongruence

Kittler, Josef and Zor, Cemre (2018) Delta divergence: A novel decision cognizant measure of classifier incongruence IEEE Transactions on Cybernetics.

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Abstract

In pattern recognition, disagreement between two classifiers regarding the predicted class membership of an observation can be indicative of an anomaly and its nuance. As in general classifiers base their decisions on class aposteriori probabilities, the most natural approach to detecting classifier incongruence is to use divergence. However, existing divergences are not particularly suitable to gauge classifier incongruence. In this paper, we postulate the properties that a divergence measure should satisfy and propose a novel divergence measure, referred to as Delta divergence. In contrast to existing measures, it focuses on the dominant (most probable) hypotheses and thus reduces the effect of the probability mass distributed over the non dominant hypotheses (clutter). The proposed measure satisfies other important properties such as symmetry, and independence of classifier confidence. The relationship of the proposed divergence to some baseline measures, and its superiority, is shown experimentally.

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
Date : 1 June 2018
Funders : EPSRC
Identification Number : 10.1109/TCYB.2018.2825353
Copyright Disclaimer : © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works
Uncontrolled Keywords : f-divergences, total variation distance, divergence clutter, classifier incongruence
Depositing User : Melanie Hughes
Date Deposited : 13 Apr 2018 08:06
Last Modified : 17 Jul 2018 12:01
URI: http://epubs.surrey.ac.uk/id/eprint/846213

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