Decision trees for knowledge representation

Mohammad Azad; Igor Chikalov; Mikhail Moshkov

Decision trees for knowledge representation

Mohammad Azad, Igor Chikalov, Mikhail Moshkov

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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.

Original language	English (US)
Title of host publication	28th International Workshop on Concurrency, Specification and Programming, CS and P 2019
Publisher	[email protected]
State	Published - 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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https://repository.kaust.edu.sa/bitstream/10754/666478/1/decision%20trees.pdf

Cite this

@inproceedings{d3693560d7374e689fee4feb00879f12,

title = "Decision trees for knowledge representation",

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.",

author = "Mohammad Azad and Igor Chikalov and Mikhail Moshkov",

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.",

year = "2019",

month = jan,

day = "1",

language = "English (US)",

booktitle = "28th International Workshop on Concurrency, Specification and Programming, CS and P 2019",

publisher = "[email protected]",

}

TY - GEN

T1 - Decision trees for knowledge representation

AU - Azad, Mohammad

AU - Chikalov, Igor

AU - Moshkov, Mikhail

N1 - 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.

PY - 2019/1/1

Y1 - 2019/1/1

N2 - 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.

AB - 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.

UR - http://hdl.handle.net/10754/666478

UR - http://ceur-ws.org/Vol-2571/CSP2019_paper_1.pdf

UR - http://www.scopus.com/inward/record.url?scp=85082111697&partnerID=8YFLogxK

M3 - Conference contribution

BT - 28th International Workshop on Concurrency, Specification and Programming, CS and P 2019

PB - [email protected]

ER -

Decision trees for knowledge representation

Abstract

Bibliographical note

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