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Proceedings Paper

Expert classifiers and the ordered veracity-experience response (OVER) curve
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Paper Abstract

The training of good generalizations must mitigate both memorization and arrogance. Memorization is characterized as being too timid in associating new observations with previous experience. Contrarily, arrogance is being too bold. In classification problems, memorization is traditionally assessed via error matrices and iterative error-based techniques such as cross validation. These techniques, however, do nothing to assess arrogance in classification. To identify arrogant classifications, we propose a confusion-based figure of merit which we shall call the ordered veracity-experience response curve, or OVER curve. To produce the OVER curve, one must employ expert classifiers. An expert is a special classifier - a relational computation with not only a mechanism for decision making but also a quantifiable skill level. In this paper, we define the elements of both the expert classifier and OVER curve and, then, demonstrate their utility using the multilayer perceptron.

Paper Details

Date Published: 30 December 2003
PDF: 12 pages
Proc. SPIE 5200, Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation VI, (30 December 2003); doi: 10.1117/12.506297
Show Author Affiliations
Amy L. Magnus, Defense Threat Reduction Agency (United States)
Mark E. Oxley, Air Force Institute of Technology (United States)


Published in SPIE Proceedings Vol. 5200:
Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation VI
Bruno Bosacchi; David B. Fogel; James C. Bezdek, Editor(s)

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