Algebraic aspects of attribute dependencies in information systems
Fundamenta Informaticae
Statistical evaluation of rough set dependency analysis
International Journal of Human-Computer Studies
Rough set approach to incomplete information systems
Information Sciences: an International Journal
Rules in incomplete information systems
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On semantic issues connected with incomplete information databases
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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Rough Sets: Theoretical Aspects of Reasoning about Data
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Rough set methods in feature selection and recognition
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On fuzzy-rough sets approach to feature selection
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On the extension of functional dependency degree from crisp to fuzzy partitions
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Information Sciences: an International Journal
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Information Sciences: an International Journal
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International Journal of Approximate Reasoning
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RSCTC'06 Proceedings of the 5th international conference on Rough Sets and Current Trends in Computing
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RSCTC'06 Proceedings of the 5th international conference on Rough Sets and Current Trends in Computing
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Information Sciences: an International Journal
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IEEE Transactions on Fuzzy Systems
On attribute reduction of rough set based on pruning rules
RSKT'10 Proceedings of the 5th international conference on Rough set and knowledge technology
Hybrid approaches to attribute reduction based on indiscernibility and discernibility relation
International Journal of Approximate Reasoning
International Journal of Approximate Reasoning
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International Journal of Approximate Reasoning
A sequential pattern mining algorithm using rough set theory
International Journal of Approximate Reasoning
International Journal of Approximate Reasoning
Probabilistic rough set over two universes and rough entropy
International Journal of Approximate Reasoning
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Pawlak's attribute dependency degree model is applicable to feature selection in pattern recognition. However, the dependency degrees given by the model are often inadequately computed as a result of the indiscernibility relation. This paper discusses an improvement to Pawlak's model and presents a new attribute dependency function. The proposed model is based on decision-relative discernibility matrices and measures how many times condition attributes are used to determine the decision value by referring to the matrix. The proposed dependency degree is computed by considering the two cases that two decision values are equal or unequal. A feature of the proposed model is that attribute dependency degrees have significant properties related to those of Armstrong's axioms. An advantage of the proposed model is that data efficiency is considered in the computation of dependency degrees. It is shown through examples that the proposed model is able to compute dependency degrees more strictly than Pawlak's model.