Greedy Algorithm for the Construction of Approximate Decision Rules for Decision Tables with Many-Valued Decisions

Mohammad Azad; Mikhail Moshkov; Beata Zielosko

doi:10.1007/978-3-662-53611-7_2

Greedy Algorithm for the Construction of Approximate Decision Rules for Decision Tables with Many-Valued Decisions

Mohammad Azad, Mikhail Moshkov, Beata Zielosko

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

Abstract

The paper is devoted to the study of a greedy algorithm for construction of approximate decision rules. 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. We consider bounds on the precision of this algorithm relative to the length of rules. To illustrate proposed approach we study a problem of recognition of labels of points in the plain. This paper contains also results of experiments with modified decision tables from UCI Machine Learning Repository.

Original language	English (US)
Title of host publication	Lecture Notes in Computer Science
Publisher	Springer Nature
Pages	24-50
Number of pages	27
ISBN (Print)	9783662536100
DOIs	https://doi.org/10.1007/978-3-662-53611-7_2
State	Published - Oct 21 2016

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10.1007/978-3-662-53611-7_2

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abstract = "The paper is devoted to the study of a greedy algorithm for construction of approximate decision rules. 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. We consider bounds on the precision of this algorithm relative to the length of rules. To illustrate proposed approach we study a problem of recognition of labels of points in the plain. This paper contains also results of experiments with modified decision tables from UCI Machine Learning Repository.",

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AB - The paper is devoted to the study of a greedy algorithm for construction of approximate decision rules. 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. We consider bounds on the precision of this algorithm relative to the length of rules. To illustrate proposed approach we study a problem of recognition of labels of points in the plain. This paper contains also results of experiments with modified decision tables from UCI Machine Learning Repository.

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Greedy Algorithm for the Construction of Approximate Decision Rules for Decision Tables with Many-Valued Decisions

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