A decision-theoretic roguth set model
Methodologies for intelligent systems, 5
A decision theoretic framework for approximating concepts
International Journal of Man-Machine Studies
Machine Learning
Probabilistic rough set approximations
International Journal of Approximate Reasoning
Learning Optimal Parameters in Decision-Theoretic Rough Sets
RSKT '09 Proceedings of the 4th International Conference on Rough Sets and Knowledge Technology
Three-way decisions with probabilistic rough sets
Information Sciences: an International Journal
The superiority of three-way decisions in probabilistic rough set models
Information Sciences: an International Journal
Probabilistic model criteria with decision-theoretic rough sets
Information Sciences: an International Journal
An optimization viewpoint of decision-theoretic rough set model
RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
Fundamenta Informaticae - Advances in Rough Set Theory
Multiple criteria decision analysis with game-theoretic rough sets
RSKT'12 Proceedings of the 7th international conference on Rough Sets and Knowledge Technology
A Multiple-category Classification Approach with Decision-theoretic Rough Sets
Fundamenta Informaticae - Rough Sets and Knowledge Technology (RSKT 2010)
Multiple criteria decision analysis with game-theoretic rough sets
RSKT'12 Proceedings of the 7th international conference on Rough Sets and Knowledge Technology
Rule measures tradeoff using game-theoretic rough sets
BI'12 Proceedings of the 2012 international conference on Brain Informatics
Fuzzy probabilistic rough set model on two universes and its applications
International Journal of Approximate Reasoning
Incorporating logistic regression to decision-theoretic rough sets for classifications
International Journal of Approximate Reasoning
An automatic method to determine the number of clusters using decision-theoretic rough set
International Journal of Approximate Reasoning
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In a probabilistic rough set model, the positive, negative and boundary regions are associated with classification errors or uncertainty. The uncertainty is controlled by a pair of thresholds defining the three regions. The problem of searching for optimal thresholds can be formulated as the minimization of uncertainty induced by the three regions. By using Shannon entropy as a measure of uncertainty, we present an information-theoretic approach to the interpretation and determination of thresholds.