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

Amy L. Magnus; Mark E. Oxley

doi:10.1117/12.506297

30 December 2003 Expert classifiers and the ordered veracity-experience response (OVER) curve

Amy L. Magnus, Mark E. Oxley

Proceedings Volume 5200, Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation VI; (2003) https://doi.org/10.1117/12.506297
Event: Optical Science and Technology, SPIE's 48th Annual Meeting, 2003, San Diego, California, United States

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.

Citation Download Citation

Amy L. Magnus and Mark E. Oxley "Expert classifiers and the ordered veracity-experience response (OVER) curve", Proc. SPIE 5200, Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation VI, (30 December 2003); https://doi.org/10.1117/12.506297

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