Uncertainty characterization using copulas for classification

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

Research output: Chapter in Book/Entry/PoemConference contribution

3 Scopus citations


We address the problem of characterizing uncertainty for multisensor data fusion in a classification problem. To achieve this goal, we model the joint density of given multivariate data using copula functions while allowing the ability to incorporate any desired marginal distributions, i.e., any desired modalities. The proposed model is data driven in that the corresponding copula functions and their parameters are learned from the data. Our results show that the proposed framework can capture the uncertainties more accurately than current state of the practice, and lead to robust and improved classification performance compared to traditional classifiers.

Original languageEnglish (US)
Title of host publicationMultisensor, Multisource Information Fusion
Subtitle of host publicationArchitectures, Algorithms, and Applications 2015
EditorsJerome J. Braun
ISBN (Electronic)9781628416145
StatePublished - 2015
EventMultisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2015 - Baltimore, United States
Duration: Apr 21 2015 → …

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X


OtherMultisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2015
Country/TerritoryUnited States
Period4/21/15 → …


  • Multisource data fusion
  • classification
  • copula theory

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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