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A decision cognizant Kullback-Leibler divergence

Ponti, M, Kittler, JV, Riva, M, De Campos, T and Zor, C (2017) A decision cognizant Kullback-Leibler divergence Pattern Recognition, 61. pp. 470-478.

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In decision making systems involving multiple classifiers there is the need to assess classifier (in)congruence, that is to gauge the degree of agreement between their outputs. A commonly used measure for this purpose is the Kullback–Leibler (KL) divergence. We propose a variant of the KL divergence, named decision cognizant Kullback–Leibler divergence (DC-KL), to reduce the contribution of the minority classes, which obscure the true degree of classifier incongruence. We investigate the properties of the novel divergence measure analytically and by simulation studies. The proposed measure is demonstrated to be more robust to minority class clutter. Its sensitivity to estimation noise is also shown to be considerably lower than that of the classical KL divergence. These properties render the DC-KL divergence a much better statistic for discriminating between classifier congruence and incongruence in pattern recognition systems.

Item Type: Article
Subjects : Electronic Engineering
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Vision Speech and Signal Processing
Authors :
Ponti, M
Kittler, JV
Riva, M
De Campos, T
Zor, C
Date : January 2017
DOI : 10.1016/j.patcog.2016.08.018
Copyright Disclaimer : © 2016. This manuscript version is made available under the CC-BY-NC-ND 4.0 license
Uncontrolled Keywords : Kullback–Leibler divergence, Divergence clutter, Classifier incongruence
Depositing User : Symplectic Elements
Date Deposited : 18 Oct 2016 15:14
Last Modified : 20 Aug 2017 02:08

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