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Proceedings Paper

Theory of confusion
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Paper Abstract

Given a classifier, presently we use a confusion matrix to quantify how much the classifier deviates from truth based upon training data. Shortcomings to this limited application of the confusion matrix are that (1) it does not communicate data trends in feature space, for example where errors congregate, and (2) the truth mapping is largely unknown except for a small, potentially biased sample set. In practice, one does not have truth but has to rely on an expert's opinion. We propose the mathematical theory of confusion comparing and contrasting the opinions of two experts (i.e., two classifiers). This theory has advantages over traditional confusion matrices in that it provides a capability for expressing classification confidence over ALL of feature space, not just at sampled truth. This theory quantifies different types of confusion between classifiers and yields a region of feature space where confusion occurs. An example using Artificial Neural Networks will be given.

Paper Details

Date Published: 14 November 2001
PDF: 12 pages
Proc. SPIE 4479, Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation IV, (14 November 2001); doi: 10.1117/12.448337
Show Author Affiliations
Amy L. Magnus, Air Force Research Lab. (United States)
Mark E. Oxley, Air Force Institute of Technology (United States)


Published in SPIE Proceedings Vol. 4479:
Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation IV
Bruno Bosacchi; David B. Fogel; James C. Bezdek, Editor(s)

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