A systematic study on attribute reduction with rough sets based on general binary relations
Information Sciences: an International Journal
Attribute reduction in decision-theoretic rough set models
Information Sciences: an International Journal
An Attribute Reduction Algorithm Based on Conditional Entropy and Frequency of Attributes
ICICTA '08 Proceedings of the 2008 International Conference on Intelligent Computation Technology and Automation - Volume 01
A New Cube-based Algorithm for Computing the Feature Core of a Consistent Decision Table
ISCID '08 Proceedings of the 2008 International Symposium on Computational Intelligence and Design - Volume 01
Discernibility matrix simplification for constructing attribute reducts
Information Sciences: an International Journal
Financial time-series analysis with rough sets
Applied Soft Computing
Attribute reduction and optimal decision rules acquisition for continuous valued information systems
Information Sciences: an International Journal
Relative reducts in consistent and inconsistent decision tables of the Pawlak rough set model
Information Sciences: an International Journal
Three-way decisions with probabilistic rough sets
Information Sciences: an International Journal
A hybrid model based on rough sets theory and genetic algorithms for stock price forecasting
Information Sciences: an International Journal
Toward boosting distributed association rule mining by data de-clustering
Information Sciences: an International Journal
Multi-variable fuzzy forecasting based on fuzzy clustering and fuzzy rule interpolation techniques
Information Sciences: an International Journal
New model for system behavior prediction based on belief rule based systems
Information Sciences: an International Journal
Solving the attribute reduction problem with ant colony optimization
Transactions on rough sets XIII
Knowledge reduction in real decision formal contexts
Information Sciences: an International Journal
A Comparative Study of Algebra Viewpoint and Information Viewpoint in Attribute Reduction
Fundamenta Informaticae
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Core set inconsistency always causes confusion regarding how to select the proper core set for data reduction in inconsistent decision tables. In this paper, partitions of knowledge granules are introduced to analyze this inconsistency, and it is concluded that there are only three types of effective partitions: those that focus on exact information, those that focus on exact, partial, and negative information and those that focus on exact, partial, negative, and probabilistic information. All useful core sets are calculated systematically by converting the three types of partitions to corresponding discernibility matrices. Then, we define three types of rules, positive, inexact, and confidence rules, based on the three types of partitions. Using these rules, an intelligible rule-based strategy is proposed to select the proper core set for a practical application, which resolves the confusion caused by core set inconsistency and completes the process of data reduction. Experimental analysis and industrial results demonstrate the effectiveness of the selection strategy.