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Upper Bounds and Algorithms for Construction of Deterministic Decision Trees for Decision Tables. Second Approach

  • Mikhail MoshkovEmail author
Chapter
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Part of the Intelligent Systems Reference Library book series (ISRL, volume 179)

Abstract

In this chapter, upper bounds on the minimum complexity and algorithms for construction of deterministic decision trees for decision tables are considered. These bounds and algorithms are based on the use of so-called additive-bounded uncertainty measures for decision tables. The bounds are true for any complexity function having the property \(\varLambda 1\). When developing algorithms, we assume that the complexity functions have properties \(\varLambda 1\), \(\varLambda 2\), and \(\varLambda 3\).

References

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    Moshkov, M.: On conditional tests. Sov. Phys. Dokl. 27, 528–530 (1982)zbMATHGoogle Scholar
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    Moshkov, M.: Conditional tests. In: Yablonskii, S.V. (ed.) Problemy Kibernetiki (in Russian), vol. 40, pp. 131–170. Nauka Publishers, Moscow (1983)Google Scholar
  3. 3.
    Moshkov, M.: Bounds on complexity and algorithms for construction of deterministic conditional tests. In: Mat. Vopr. Kibern. (in Russian), vol. 16, pp. 79–124. Fizmatlit, Moscow (2007). http://library.keldysh.ru/mvk.asp?id=2007-79

Copyright information

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Computer, Electrical and Mathematical Science and Engineering DivisionKing Abdullah University of Science and TechnologyThuwalSaudi Arabia

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