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Factoring a priori classifier performance into decision fusion

Goebel, Kai and Mysore, Shreesh P. (2002) Factoring a priori classifier performance into decision fusion. In: Sensor Fusion: Architectures, Algorithms, and Applications VI. Proceedings of SPIE. No.4731. Society of Photo-optical Instrumentation Engineers (SPIE) , Bellingham, WA, pp. 10-21. ISBN 9780819444813. http://resolver.caltech.edu/CaltechAUTHORS:20181213-143635090

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Abstract

In this paper we present methods to enhance the classification rate in decision fusion with partially redundant information by manipulating the input to the fusion scheme using a priori performance information. Intuitively, it seems to make sense to trust a more reliable tool more than a less reliable one without discounting the less reliable one completely. For a multi-class classifier, the reliability per class must be considered. In addition, complete ignorance for any given class must also be factored into the fusion process to ensure that all faults are equally well represented. However, overly trusting the best classifier will not permit the fusion tool to achieve results that rate beyond the best classifiers performance. We assume that the performance of classifiers to be fused is known, and show how to take advantage of this information. In particular, we glean pertinent performance information from the classifier confusion matrices and their cousin, the relevance matrix. We further demonstrate how to integrate a priori performance information within an hierarchical fusion architecture. We investigate several schemes for these operations 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 present results motivated from diagnosing on-board faults in aircraft engines.


Item Type:Book Section
Related URLs:
URLURL TypeDescription
https://doi.org/10.1117/12.458386DOIArticle
Additional Information:© 2002 Society of Photo-Optical Instrumentation Engineers (SPIE). This research was in part supported by DARPA project MDA 972-98-3-0002. The authors also greatfully acknowledge the comments of Malcolm Ashby, Kiyoung Chung, Vivek Badami, Michael Krok, and Hunt Sutherland.
Funders:
Funding AgencyGrant Number
Defense Advanced Research Projects Agency (DARPA)MDA 972-98-3-0002
Subject Keywords:Classification; Diagnostics; Information Fusion; Decision Fusion; A priori Information; Confusion Matrix
Record Number:CaltechAUTHORS:20181213-143635090
Persistent URL:http://resolver.caltech.edu/CaltechAUTHORS:20181213-143635090
Official Citation:Kai Goebel, Shreesh P. Mysore, "Factoring a priori classifier performance into decision fusion," Proc. SPIE 4731, Sensor Fusion: Architectures, Algorithms, and Applications VI, (6 March 2002); doi: 10.1117/12.458386
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:91835
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:13 Dec 2018 22:49
Last Modified:13 Dec 2018 22:49

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