Rough computational methods for information systems
Artificial Intelligence
Various approaches to reasoning with frequency based decision reducts: a survey
Rough set methods and applications
Rough sets and intelligent data analysis
Information Sciences—Informatics and Computer Science: An International Journal
Fundamenta Informaticae
A Heuristic Algorithm for Attribute Reduction Based on Discernibility and Equivalence by Attributes
MDAI '09 Proceedings of the 6th International Conference on Modeling Decisions for Artificial Intelligence
Knowledge reduction based on granular computing from decision information systems
RSKT'10 Proceedings of the 5th international conference on Rough set and knowledge technology
An Evaluation Method of Relative Reducts Based on Roughness of Partitions
International Journal of Cognitive Informatics and Natural Intelligence
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The current reduction algorithms based on rough sets still have some disadvantages. First, we indicated their limitations for reduct generation. We modified the mean decision power, and proposed to use the algebraic definition of decision power. To select optimal attribute reduction, the judgment criterion of decision with inequality was presented and some important conclusions were obtained. A complete algorithm for the attribute reduction was designed. Finally, through analyzing the given example, it was shown that the proposed heuristic information was better and more efficient than the others, and the presented in the paper method reduces time complexity and improves the performance. We report experimental results with several data sets from UCI repository and we compare the results with some other methods. The results prove that the proposed method is promising.