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Application of Bayesian Decision Theory to Constrained Classification Networks

  • Hans J. Vos
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

Abstract

A classification network of classifying subjects as either suitable or un­suitable for a treatment followed by classifying accepted subjects as either a master or nonmaster will be formalized in the case of several relevant subpopulations. It will further be assumed that only a fixed number of subjects can be accepted for the treatment. The purpose of this paper is to optimize simultaneously this constrained classification network using Bayesian decision theory. In doing so, important distinctions will be made between weak and strong as well as monotone and nonmonotone decision rules. Also, a theorem will be given under what conditions optimal (weak) rules will be monotone.

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References

  1. LEHMANN, E.L. (1959): Testing Statistical Hypotheses. Wiley, New York.Google Scholar
  2. MELLENBERGH, G.J. and VAN DER LINDEN, W.J. (1981): The Linear Utility Model for Optimal Selection. Psychometrika, 46, 283–293.CrossRefGoogle Scholar
  3. VOS, H.J. (2000): A Minimax Solution for Sequential Classification Problems. In: H.A.L. KIERS, J.-P. RASSON, P.J.F. GROENEN, and M. SCHADERS (Eds.): Data Analysis, Classification, and Related Methods. Springer, Heidelberg, 101–106.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Hans J. Vos
    • 1
  1. 1.Department of Educational Measurement and Data AnalysisUniversity of TwenteEnschedeThe Netherlands

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