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Distinguishability and Attribute Reduction for Entire-Granulation Rough Sets
Pages: 699-708
Year: Issue:  8
Journal: Pattern Recognition and Artificial Intelligence

Keyword:  Entire-Granulation Rough SetsDistinguishabilityAttribute ReductionPositive Region DistinguishabilityEntire-Granulation Pawlak Reduct;
Abstract: It is difficult to calculate attribute reduct due to high time and space complexity of entire-granulation rough sets. To solve this problem, distinguishability in information systems is defined by equivalent class, and its properties are studied. It is proved that the attribute reduct based on distinguishability is equivalent to absolute reduct. The positive region distinguishability in decision systems is defined and its properties are discussed. It is also proved that positive region distinguishability reduct is a superset of entire-granulation Pawlak reduct, but in most cases it is equal to entire-granulation Pawlak reduct and it can be regarded as an approximation of entire-granulation Pawlak reduct. Theoretical analysis and experiments show that compared with other attribute reduction algorithms, positive region distinguishability reduct has great advantages in computational complexity and classification accuracy.
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