Decision trees for knowledge representation

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Type
Conference Paper
Authors
Azad, Mohammad
Chikalov, Igor
Moshkov, Mikhail cc
KAUST Department
Office of the VP
Computational Bioscience Research Center (CBRC)
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Academic Affairs
Applied Mathematics and Computational Science Program
Date
2019-01-01
Permanent link to this record
http://hdl.handle.net/10754/666478

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Abstract
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.
Sponsors
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.
Publisher
CEUR-WSceurws@sunsite.informatik.rwth-aachen.de
Conference/Event name
28th International Workshop on Concurrency, Specification and Programming, CS and P 2019
Additional Links
http://ceur-ws.org/Vol-2571/CSP2019_paper_1.pdf
Collections
Conference Papers; Applied Mathematics and Computational Science Program; Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
Copyright c 2019 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
Except where otherwise noted, this item's license is described as Copyright c 2019 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).