Taking advantage of misclassifications to boost classification rate in decision fusion

Kai Goebel, Shreesh P. Mysore

Research output: Contribution to journalArticlepeer-review


This paper presents methods to boost the classification rate in decision fusion with partially redundant information. This is accomplished by utilizing the information of known misclassifications of certain classes to systematically modify class output. For example, if it is known beforehand that tool A misclassifies class 1 often as class 2, then it appears to be prudent to integrate that information into the reasoning process if class 1 is indicated by tool B and class 2 is observed by tool A. Particularly this preferred misclassification information is contained in the asymmetric (cross-correlation) entries of the confusion matrix. An operation we call "cross-correlation" is performed where this information is explicitly used to modify class output before the first fused estimate is calculated. We investigate several methods for cross-correlation and discuss the advantages and disadvantages of each. We then apply the concepts introduced to the diagnostic realm where we aggregate the output of several different diagnostic tools. We show how the proposed approach fits into an information fusion architecture and finally present results motivated from diagnosing on-board faults in aircraft engines.

Original languageEnglish (US)
Pages (from-to)11-20
Number of pages10
JournalProceedings of SPIE - The International Society for Optical Engineering
StatePublished - 2001
Externally publishedYes


  • Classification
  • Correlation
  • Cross-Correlation
  • Decision Fusion
  • Diagnostics
  • Information Fusion

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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