Decision trees for knowledge representation

Mohammad Azad, Igor Chikalov, Mikhail Moshkov

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations


In this paper, we consider decision trees as a means of knowledge representation. To this end, we design three algorithms for decision tree construction that are based on extensions of dynamic programming. We study three parameters of the decision trees constructed by these algorithms: number of nodes, global misclassification rate, and local misclassification rate.
Original languageEnglish (US)
Title of host publication28th International Workshop on Concurrency, Specification and Programming, CS and P 2019
StatePublished - Jan 1 2019

Bibliographical note

KAUST Repository Item: Exported on 2020-12-19
Acknowledgements: Research reported in this publication was supported by King Abdullah University of Science and Technology (KAUST). The authors are greatly indebted to the anonymous reviewers for useful comments.


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