A threshold of ln n for approximating set cover (preliminary version)
STOC '96 Proceedings of the twenty-eighth annual ACM symposium on Theory of computing
STOC '97 Proceedings of the twenty-ninth annual ACM symposium on Theory of computing
&agr;-RST: a generalization of rough set theory
Information Sciences—Informatics and Computer Science: An International Journal
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Computational Complexity of Machine Learning
Computational Complexity of Machine Learning
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
Fundamenta Informaticae
Approximation algorithms for set cover and related problems
Approximation algorithms for set cover and related problems
Normalized Decision Functions and Measures for Inconsistent Decision Tables Analysis
Fundamenta Informaticae
Ensembles of Classifiers Based on Approximate Reducts
Fundamenta Informaticae - Concurrency Specification and Programming (CS&P'2000)
Order based genetic algorithms for the search of approximate entropy reducts
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
Universal Attribute Reduction Problem
RSEISP '07 Proceedings of the international conference on Rough Sets and Intelligent Systems Paradigms
Universal Problem of Attribute Reduction
Transactions on Rough Sets IX
Greedy Algorithms withWeights for Construction of Partial Association Rules
Fundamenta Informaticae
On partial covers, reducts and decision rules
Transactions on rough sets VIII
Greedy Algorithms withWeights for Construction of Partial Association Rules
Fundamenta Informaticae
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In the paper the accuracy of greedy algorithms with weights for construction of partial covers, reducts and decision rules is considered. Bounds on minimal weight of partial covers, reducts and decision rules based on an information on greedy algorithm work are studied. Results of experiments with software implementation of greedy algorithms are described.