Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Approximate Reducts and Association Rules - Correspondence and Complexity Results
RSFDGrC '99 Proceedings of the 7th International Workshop on New Directions in Rough Sets, Data Mining, and Granular-Soft Computing
Partial Covers, Reducts and Decision Rules in Rough Sets: Theory and Applications
Partial Covers, Reducts and Decision Rules in Rough Sets: Theory and Applications
Fundamenta Informaticae
Combinatorial Machine Learning: A Rough Set Approach
Combinatorial Machine Learning: A Rough Set Approach
Fundamenta Informaticae - Concurrency, Specification and Programming
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The paper is devoted to the study of a greedy algorithm for construction of approximate tests super-reducts. 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 cardinality of tests.