Tolerance approximation spaces
Fundamenta Informaticae - Special issue: rough sets
Fuzzy Similarity Relation as a Basis for Rough Approximations
RSCTC '98 Proceedings of the First International Conference on Rough Sets and Current Trends in Computing
On the Rough Realations: An Alternative Formulation
RSFDGrC '99 Proceedings of the 7th International Workshop on New Directions in Rough Sets, Data Mining, and Granular-Soft Computing
On the Extension of Rough Sets under Incomplete Information
RSFDGrC '99 Proceedings of the 7th International Workshop on New Directions in Rough Sets, Data Mining, and Granular-Soft Computing
On the Unknown Attribute Values in Learning from Examples
ISMIS '91 Proceedings of the 6th International Symposium on Methodologies for Intelligent Systems
A characterization of PQI interval orders
Discrete Applied Mathematics - Special issue: The 1998 conference on ordinal and symbolic data analysis (OSDA '98)
Induction of Decision Rules and Classification in the Valued Tolerance Approach
TSCTC '02 Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing
Valued Hesitation in Intervals Comparison
SUM '07 Proceedings of the 1st international conference on Scalable Uncertainty Management
Rough sets handling missing values probabilistically interpreted
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part I
Checking whether or not rough-set-based methods to incomplete data satisfy a correctness criterion
MDAI'05 Proceedings of the Second international conference on Modeling Decisions for Artificial Intelligence
Algebraic structures for rough sets
Transactions on Rough Sets II
A fast feature selection approach based on rough set boundary regions
Pattern Recognition Letters
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The concept of valuedtolerance is introducedas an extension of the usual concept of indiscernibility (which is a crisp equivalence relation) in rough sets theory. Some specific properties of the approach are discussed. Further on the problem of inducing rules is addressed. Properties of a "credibility degree" associated to each rule are analysed and its use in classification problems is discussed.