Copula Based Classifier Fusion Under Statistical Dependence

Onur Ozdemir; Thomas Allen; Sora Choi; Thakshila Wimalajeewa; Pramod Kumar Varshney

doi:10.1109/TPAMI.2017.2774300

Copula Based Classifier Fusion Under Statistical Dependence

Onur Ozdemir, Thomas Allen, Sora Choi, Thakshila Wimalajeewa, Pramod Kumar Varshney

Department of Electrical Engineering & Computer Science

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

5 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)
Journal	IEEE Transactions on Pattern Analysis and Machine Intelligence
DOIs	https://doi.org/10.1109/TPAMI.2017.2774300
State	Accepted/In press - Nov 15 2017

Keywords

classification
Copulas
Data models
Electronic mail
Probability
Probability density function
probability score fusion
Sensor fusion
statistical dependence
Training data

ASJC Scopus subject areas

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

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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.",

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AU - Allen, Thomas

AU - Choi, Sora

AU - Wimalajeewa, Thakshila

AU - Varshney, Pramod Kumar

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AB - 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.

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KW - Data models

KW - Electronic mail

KW - Probability

KW - Probability density function

KW - probability score fusion

KW - Sensor fusion

KW - statistical dependence

KW - Training data

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