Copula Based Classifier Fusion Under Statistical Dependence

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

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


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 languageEnglish (US)
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
StateAccepted/In press - Nov 15 2017


  • 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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