Ponti, M;
Kittler, J;
Riva, M;
de Campos, T;
Zor, C;
(2017)
A decision cognizant Kullback-Leibler divergence.
Pattern Recognition
, 61
pp. 470-478.
10.1016/j.patcog.2016.08.018.
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Abstract
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.
Type: | Article |
---|---|
Title: | A decision cognizant Kullback-Leibler divergence |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.1016/j.patcog.2016.08.018 |
Publisher version: | https://doi.org/10.1016/j.patcog.2016.08.018 |
Language: | English |
Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
Keywords: | Kullback–Leibler divergence, Divergence clutter, Classifier incongruence |
UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
URI: | https://discovery.ucl.ac.uk/id/eprint/10067227 |
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