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|
|Number of pages||5|
|State||Published - Oct 19 2022|
Bibliographical noteKAUST Repository Item: Exported on 2022-12-14
Acknowledgements: 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.