Copula Based Classifier Fusion under Statistical Dependence

Onur Ozdemir; Thomas G. Allen; Sora Choi; Thakshila Wimalajeewa; Pramod K. Varshney

doi:10.1109/TPAMI.2017.2774300

Copula Based Classifier Fusion under Statistical Dependence

Onur Ozdemir, Thomas G. Allen, Sora Choi, Thakshila Wimalajeewa, Pramod K. Varshney

Department of Electrical Engineering & Computer Science

Research output: Contribution to journal › Article › peer-review

20 Scopus citations

Abstract

We consider the problem of fusing probability scores from a set of classifiers to estimate a final fused probability score. Our interest is in scenarios where the classifiers are statistically dependent. To that end, we propose a new classifier fusion approach that is data driven and founded on the statistical theory of copulas. Numerical results with both simulated and real data show that our copula based classifier fusion approach produces better probability scores than individual classifiers and outperforms existing probability score fusion approaches.

Original language	English (US)
Article number	8113592
Pages (from-to)	2740-2748
Number of pages	9
Journal	IEEE Transactions on Pattern Analysis and Machine Intelligence
Volume	40
Issue number	11
DOIs	https://doi.org/10.1109/TPAMI.2017.2774300
State	Published - Nov 1 2018

Keywords

Copulas
classification
probability score fusion
statistical dependence

ASJC Scopus subject areas

Software
Computer Vision and Pattern Recognition
Computational Theory and Mathematics
Artificial Intelligence
Applied Mathematics

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10.1109/TPAMI.2017.2774300

Cite this

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Copula Based Classifier Fusion under Statistical Dependence

Abstract

Keywords

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