On algorithm for building of optimal α-decision trees

Abdulaziz Alkhalid; Igor Chikalov; Mikhail Moshkov

doi:10.1007/978-3-642-13529-3_47

On algorithm for building of optimal α-decision trees

Abdulaziz Alkhalid, Igor Chikalov, Mikhail Moshkov

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

22 Scopus citations

Abstract

The paper describes an algorithm that constructs approximate decision trees (α-decision trees), which are optimal relatively to one of the following complexity measures: depth, total path length or number of nodes. The algorithm uses dynamic programming and extends methods described in [4] to constructing approximate decision trees. Adjustable approximation rate allows controlling algorithm complexity. The algorithm is applied to build optimal α-decision trees for two data sets from UCI Machine Learning Repository [1]. © 2010 Springer-Verlag Berlin Heidelberg.

Original language	English (US)
Title of host publication	Rough Sets and Current Trends in Computing
Publisher	Springer Nature
Pages	438-445
Number of pages	8
ISBN (Print)	3642135285; 9783642135286
DOIs	https://doi.org/10.1007/978-3-642-13529-3_47
State	Published - 2010

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

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10.1007/978-3-642-13529-3_47

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title = "On algorithm for building of optimal α-decision trees",

abstract = "The paper describes an algorithm that constructs approximate decision trees (α-decision trees), which are optimal relatively to one of the following complexity measures: depth, total path length or number of nodes. The algorithm uses dynamic programming and extends methods described in [4] to constructing approximate decision trees. Adjustable approximation rate allows controlling algorithm complexity. The algorithm is applied to build optimal α-decision trees for two data sets from UCI Machine Learning Repository [1]. {\textcopyright} 2010 Springer-Verlag Berlin Heidelberg.",

author = "Abdulaziz Alkhalid and Igor Chikalov and Mikhail Moshkov",

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doi = "10.1007/978-3-642-13529-3_47",

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AU - Chikalov, Igor

AU - Moshkov, Mikhail

N1 - KAUST Repository Item: Exported on 2020-10-01

PY - 2010

Y1 - 2010

N2 - The paper describes an algorithm that constructs approximate decision trees (α-decision trees), which are optimal relatively to one of the following complexity measures: depth, total path length or number of nodes. The algorithm uses dynamic programming and extends methods described in [4] to constructing approximate decision trees. Adjustable approximation rate allows controlling algorithm complexity. The algorithm is applied to build optimal α-decision trees for two data sets from UCI Machine Learning Repository [1]. © 2010 Springer-Verlag Berlin Heidelberg.

AB - The paper describes an algorithm that constructs approximate decision trees (α-decision trees), which are optimal relatively to one of the following complexity measures: depth, total path length or number of nodes. The algorithm uses dynamic programming and extends methods described in [4] to constructing approximate decision trees. Adjustable approximation rate allows controlling algorithm complexity. The algorithm is applied to build optimal α-decision trees for two data sets from UCI Machine Learning Repository [1]. © 2010 Springer-Verlag Berlin Heidelberg.

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UR - http://link.springer.com/10.1007/978-3-642-13529-3_47

UR - http://www.scopus.com/inward/record.url?scp=79956262692&partnerID=8YFLogxK

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DO - 10.1007/978-3-642-13529-3_47

M3 - Conference contribution

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SP - 438

EP - 445

BT - Rough Sets and Current Trends in Computing

PB - Springer Nature

ER -

On algorithm for building of optimal α-decision trees

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