Uncertainty characterization using copulas for classification

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

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 publicationProceedings of SPIE - The International Society for Optical Engineering
PublisherSPIE
Volume9498
ISBN (Print)9781628416145
DOIs
StatePublished - 2015
EventMultisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2015 - Baltimore, United States
Duration: Apr 21 2015 → …

Other

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

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Keywords

  • classification
  • copula theory
  • Multisource data fusion

ASJC Scopus subject areas

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

Cite this

Ozdemir, O., Choi, S., Allen, T. G., & Varshney, P. K. (2015). Uncertainty characterization using copulas for classification. In Proceedings of SPIE - The International Society for Optical Engineering (Vol. 9498). [94980A] SPIE. https://doi.org/10.1117/12.2181908