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) |
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Journal | IEEE Transactions on Pattern Analysis and Machine Intelligence |
DOIs | |
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