Abstract
In this paper, we assume that a dispersed data is represented by a finite set S of decision tables with equal sets of attributes. We discuss one of the possible ways to the study decision trees common to all tables from the set S: building a decision table for which the set of decision trees coincides with the set of decision trees common to all tables from S. We show when we can build such a decision table and how to build it in a polynomial time. If we have such a table, we can apply to it various decision tree learning algorithms. We extend the considered approach to the study of decision rules and test (reducts) common to all tables from S.
Original language | English (US) |
---|---|
Title of host publication | Procedia Computer Science |
Publisher | Elsevier BV |
Pages | 2503-2507 |
Number of pages | 5 |
DOIs | |
State | Published - Oct 19 2022 |
Bibliographical note
KAUST Repository Item: Exported on 2022-12-14Acknowledgements: Research reported in this publication was supported by King Abdullah University of Science and Technology (KAUST). The author is greatly indebted to the anonymous reviewers for useful comments and suggestions.