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A Minimax Solution for Sequential Classification Problems

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

Abstract

The purpose of this paper is to derive optimal rules for sequential classification problems. In a sequential classification test, for instance, in an educational context, the decision is to classify a student as a master, a partial master, a nonmaster, or continue testing and administering another random item. The framework of minimax sequential decision theory is used by minimizing the maximum expected losses associated with all possible decision rules at each stage of testing. The main advantage of this approach is that costs of testing can be explicitly taken into account.

Keywords

Classification Decision Optimal Rule Posterior Predictive Distribution Sequential Classification Loss Structure 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. COOMBS, C.H., DAWES, R.M., and TVERSKY, A. (1970):Mathematical Psychology: An Elementary Introduction. Englewood Cliffs, New Yersey.Google Scholar
  2. FERGUSON, T.S. (1967): Mathematical Statistics: A Decision Theoretic Approach. Academic Press, New York.Google Scholar
  3. NEDELSKY, L. (1954): Absolute Grading Standards for Objective Tests. Educational and Psychological Measurement, 14, 3–19.CrossRefGoogle Scholar
  4. VOS, H.J. (1998): Compensatory Rules for Optimal Classification With Mastery Scores. In: A. Rizzi, M. Vichy, and H.-H. Bock (Eds.): Advances in Data Science and Classification. Springer, Heidelberg, 211–218.Google Scholar

Copyright information

© Springer-Verlag Berlin · Heidelberg 2000

Authors and Affiliations

  • Hans J. Vos
    • 1
  1. 1.Faculty of Educational Science and TechnologyTwente UniversityEnschedeThe Netherlands

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