eprintid: 10067254
rev_number: 18
eprint_status: archive
userid: 608
dir: disk0/10/06/72/54
datestamp: 2019-02-07 18:03:55
lastmod: 2021-09-20 22:36:51
status_changed: 2019-02-07 18:03:55
type: article
metadata_visibility: show
creators_name: Kittler, J
creators_name: Zor, C
title: Delta Divergence: A Novel Decision Cognizant Measure of Classifier Incongruence
ispublished: pub
divisions: UCL
divisions: B04
divisions: C05
divisions: F48
keywords: Classifier incongruence, divergence clutter,
f-divergences, total variation distance
note: This work is licensed under a Creative Commons Attribution 3.0 License. For more information, see http://creativecommons.org/licenses/by/3.0/
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. Since, in general, classifiers base their decisions on class a posteriori 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.
date: 2019-06
date_type: published
official_url: http://doi.org/10.1109/TCYB.2018.2825353
oa_status: green
full_text_type: pub
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 1622593
doi: 10.1109/TCYB.2018.2825353
lyricists_name: Zor, Cemre
lyricists_id: CZORX93
actors_name: Flynn, Bernadette
actors_id: BFFLY94
actors_role: owner
full_text_status: public
publication: IEEE Transactions on Cybernetics
volume: 49
number: 6
pagerange: 2331-2343
event_location: United States
issn: 2168-2275
citation:        Kittler, J;    Zor, C;      (2019)    Delta Divergence: A Novel Decision Cognizant Measure of Classifier Incongruence.                   IEEE Transactions on Cybernetics , 49  (6)   pp. 2331-2343.    10.1109/TCYB.2018.2825353 <https://doi.org/10.1109/TCYB.2018.2825353>.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10067254/1/08370682.pdf