Copula Based Classifier Fusion under Statistical Dependence

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

Research output: Contribution to journalArticlepeer-review

23 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)
Article number8113592
Pages (from-to)2740-2748
Number of pages9
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Issue number11
StatePublished - Nov 1 2018


  • 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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