Greedy algorithm of decision tree construction for real data tables

Mikhail Ju Moshkov

doi:10.1007/978-3-540-27794-1_7

Greedy algorithm of decision tree construction for real data tables

Mikhail Ju Moshkov^*

^*Corresponding author for this work

Computer, Electrical and Mathematical Sciences and Engineering

Research output: Contribution to journal › Article › peer-review

6 Scopus citations

Abstract

In the paper a greedy algorithm for minimization of decision tree depth is described and bounds on the algorithm precision are considered. This algorithm is applicable to data tables with both discrete and continuous variables which can have missing values. Under some natural assumptions on the class N P and on the class of considered tables, the algorithm is, apparently, close to best approximate polynomial algorithms for minimization of decision tree depth.

Original language	English (US)
Pages (from-to)	161-168
Number of pages	8
Journal	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3100
DOIs	https://doi.org/10.1007/978-3-540-27794-1_7
State	Published - Jan 1 2004

Keywords

Data table
Decision table
Decision tree
Depth

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science(all)

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10.1007/978-3-540-27794-1_7

Cite this

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abstract = "In the paper a greedy algorithm for minimization of decision tree depth is described and bounds on the algorithm precision are considered. This algorithm is applicable to data tables with both discrete and continuous variables which can have missing values. Under some natural assumptions on the class N P and on the class of considered tables, the algorithm is, apparently, close to best approximate polynomial algorithms for minimization of decision tree depth.",

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AB - In the paper a greedy algorithm for minimization of decision tree depth is described and bounds on the algorithm precision are considered. This algorithm is applicable to data tables with both discrete and continuous variables which can have missing values. Under some natural assumptions on the class N P and on the class of considered tables, the algorithm is, apparently, close to best approximate polynomial algorithms for minimization of decision tree depth.

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KW - Decision table

KW - Decision tree

KW - Depth

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JO - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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ER -

Greedy algorithm of decision tree construction for real data tables

Abstract

Keywords

ASJC Scopus subject areas

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