A greedy algorithm for construction of decision trees for tables with many-valued decisions - A comparative study

Mohammad Azad; Igor Chikalov; Mikhail Moshkov; Beata Zielosko

doi:10.3233/FI-2013-929

A greedy algorithm for construction of decision trees for tables with many-valued decisions - A comparative study

Mohammad Azad, Igor Chikalov, Mikhail Moshkov, Beata Zielosko

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

1 Scopus citations

Abstract

In the paper, we study a greedy algorithm for construction of decision trees. This algorithm is applicable to decision tables with many-valued decisions where each row is labeled with a set of decisions. For a given row, we should find a decision from the set attached to this row. Experimental results for data sets from UCI Machine Learning Repository and randomly generated tables are presented. We make a comparative study of the depth and average depth of the constructed decision trees for proposed approach and approach based on generalized decision. The obtained results show that the proposed approach can be useful from the point of view of knowledge representation and algorithm construction.

Original language	English (US)
Pages (from-to)	1-15
Number of pages	15
Journal	Fundamenta Informaticae
Volume	128
Issue number	1-2
DOIs	https://doi.org/10.3233/FI-2013-929
State	Published - Nov 25 2013

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01

ASJC Scopus subject areas

Computational Theory and Mathematics
Algebra and Number Theory
Theoretical Computer Science
Information Systems

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AU - Zielosko, Beata

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PY - 2013/11/25

Y1 - 2013/11/25

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AB - In the paper, we study a greedy algorithm for construction of decision trees. This algorithm is applicable to decision tables with many-valued decisions where each row is labeled with a set of decisions. For a given row, we should find a decision from the set attached to this row. Experimental results for data sets from UCI Machine Learning Repository and randomly generated tables are presented. We make a comparative study of the depth and average depth of the constructed decision trees for proposed approach and approach based on generalized decision. The obtained results show that the proposed approach can be useful from the point of view of knowledge representation and algorithm construction.

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A greedy algorithm for construction of decision trees for tables with many-valued decisions - A comparative study

Abstract

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