Taking advantage of misclassifications to boost classification rate in decision fusion

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 mis- classifications of certain classes to systematically modify class output. For example,, if it is known beforehand that tool A mis- classifies class 1 as 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 mis-classification 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