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Predicting Effectiveness of Bayesian Classification Systems

Published online by Cambridge University Press:  01 January 2025

Louis M. Herman  and
Michael B. Dollinger
Louis M. Herman
Affiliation:
Queens College, New York
Michael B. Dollinger
Affiliation:
University of Illinois
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Abstract

A model is presented for evaluating potential effectiveness of a Bayesian classification system using the expected value of the posterior probability for true classifications as an evaluation metric. For a given set of input parameters, the value of this complex metric is predictable from a simply computed row variance metric. Prediction equations are given for several representative sets of input parameters.

Type
Original Paper
Information
Psychometrika , Volume 31 , Issue 3 , September 1966 , pp. 341 - 349
Copyright
Copyright © 1966 Psychometric Society

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Footnotes

*

Now at the University of Hawaii.

References

Chernoff, H. and Moses, L. E. Elementary decision theory, New York: Wiley, 1959.Google Scholar
Edwards, W., Lindman, H. A., and Savage, L. J. Bayesian statistical inference for psychological research. Psychol. Rev., 1963, 70, 193–242.CrossRefGoogle Scholar
Southard, J. F., Schum, D. A., and Briggs, G. E. An application of Bayes theorem as a hypotheses-selection aid in a complex information-processing system. Wright-Patterson AFB, Ohio. Behavioral Sciences Laboratory: (AMRL-TDR-64-51), 1964.CrossRefGoogle Scholar