Incomplete Information in Relational Databases
Journal of the ACM (JACM)
Fuzzy sets in approximate reasoning, part 1: inference with possibility distributions
Fuzzy Sets and Systems - Special memorial volume on foundations of fuzzy reasoning
Rules in incomplete information systems
Information Sciences: an International Journal
Rough approximation quality revisited
Artificial Intelligence
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Current Approaches to Handling Imperfect Information in Data and Knowledge Bases
IEEE Transactions on Knowledge and Data Engineering
On the Unknown Attribute Values in Learning from Examples
ISMIS '91 Proceedings of the 6th International Symposium on Methodologies for Intelligent Systems
Applying Rough Sets to Data Tables Containing Missing Values
RSEISP '07 Proceedings of the international conference on Rough Sets and Intelligent Systems Paradigms
Applying Rough Sets to Information Tables Containing Probabilistic Values
MDAI '07 Proceedings of the 4th international conference on Modeling Decisions for Artificial Intelligence
Rules and Apriori Algorithm in Non-deterministic Information Systems
Transactions on Rough Sets IX
Lower and upper approximations in data tables containing possibilistic information
Transactions on rough sets VII
Applying rough sets to information tables containing possibilistic values
Transactions on computational science II
Applying rough sets to data tables containing possibilistic information
RSKT'06 Proceedings of the First international conference on Rough Sets and Knowledge Technology
RSCTC'06 Proceedings of the 5th international conference on Rough Sets and Current Trends in Computing
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
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Methods based on rough sets to data containing incomplete information are examined under a possibilty-based interpretation for whether a correctness criterion is satisfied or not. The correctness criterion is to give the same results as methods by possible tables. The methods proposed so far do not give the same results as methods by possible tables. Therefore, we show a new formula not using implication operators in methods by valued tolerance relations. The formula bears the results that agree with ones from using possible tables. Thus, by using the formula the methods by valued tolerance relations satisfy the correctness criterion.