Tolerance approximation spaces
Fundamenta Informaticae - Special issue: rough sets
Extensions and intentions in the rough set theory
Information Sciences: an International Journal
Constructive and algebraic methods of the theory of rough sets
Information Sciences: an International Journal
Relational interpretations of neighborhood operators and rough set approximation operators
Information Sciences—Informatics and Computer Science: An International Journal
Rough set approach to incomplete information systems
Information Sciences: an International Journal
Rules in incomplete information systems
Information Sciences: an International Journal
Rough set theory applied to (fuzzy) ideal theory
Fuzzy Sets and Systems
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
A Generalized Definition of Rough Approximations Based on Similarity
IEEE Transactions on Knowledge and Data Engineering
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
Reduction and axiomization of covering generalized rough sets
Information Sciences: an International Journal
Approaches to knowledge reduction based on variable precision rough set model
Information Sciences—Informatics and Computer Science: An International Journal - Mining stream data
Topological approaches to covering rough sets
Information Sciences: an International Journal
Knowledge reduction based on the equivalence relations defined on attribute set and its power set
Information Sciences: an International Journal
Information Sciences: an International Journal
Information Sciences: an International Journal
Modeling a resource contention in the management of virtual organizations
Information Sciences: an International Journal
Information Sciences: an International Journal
Extension of covering approximation space and its application in attribute reduction
RSKT'10 Proceedings of the 5th international conference on Rough set and knowledge technology
On lower and upper intension order relations by different cover concepts
Information Sciences: an International Journal
Transversal and function matroidal structures of covering-based rough sets
RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
Bipartite graphs and coverings
RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
The reduction and fusion of fuzzy covering systems based on the evidence theory
International Journal of Approximate Reasoning
A rough set approach for estimating correlation measures in quality function deployment
Information Sciences: an International Journal
Covering based rough set approximations
Information Sciences: an International Journal
Attribute Reduction Using Extension of Covering Approximation Space
Fundamenta Informaticae - Rough Sets and Knowledge Technology (RSKT 2010)
Quantitative analysis for covering-based rough sets through the upper approximation number
Information Sciences: an International Journal
Analysis of association rule mining on quantitative concept lattice
AICI'12 Proceedings of the 4th international conference on Artificial Intelligence and Computational Intelligence
Relationships between covering-based rough sets and relation-based rough sets
Information Sciences: an International Journal
Four matroidal structures of covering and their relationships with rough sets
International Journal of Approximate Reasoning
International Journal of Approximate Reasoning
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In this paper, we propose some new approaches for attribute reduction in covering decision systems from the viewpoint of information theory. Firstly, we introduce information entropy and conditional entropy of the covering and define attribute reduction by means of conditional entropy in consistent covering decision systems. Secondly, in inconsistent covering decision systems, the limitary conditional entropy of the covering is proposed and attribute reductions are defined. And finally, by the significance of the covering, some algorithms are designed to compute all the reducts of consistent and inconsistent covering decision systems. We prove that their computational complexity are polynomial. Numerical tests show that the proposed attribute reductions accomplish better classification performance than those of traditional rough sets. In addition, in traditional rough set theory, MIBARK-algorithm [G.Y. Wang, H. Hu, D. Yang, Decision table reduction based on conditional information entropy, Chinese J. Comput., 25 (2002) 1-8] cannot ensure the reduct is the minimal attribute subset which keeps the decision rule invariant in inconsistent decision systems. Here, we solve this problem in inconsistent covering decision systems.