Comparison of the probabilistic approximate classification and the fuzzy set model
Fuzzy Sets and Systems
Rough sets: probabilistic versus deterministic approach
International Journal of Man-Machine Studies
A decision theoretic framework for approximating concepts
International Journal of Man-Machine Studies
Variable precision rough set model
Journal of Computer and System Sciences
Advances in the Dempster-Shafer theory of evidence
Key concepts in model selection: performance and generalizability
Journal of Mathematical Psychology
Criteria for choosing a rough set model
Computers & Mathematics with Applications
Three-way decisions with probabilistic rough sets
Information Sciences: an International Journal
Decision-theoretic rough set models
RSKT'07 Proceedings of the 2nd international conference on Rough sets and knowledge technology
Transactions on Rough Sets III
Probabilistic model criteria with decision-theoretic rough sets
Information Sciences: an International Journal
Analysis of data-driven parameters in game-theoretic rough sets
RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
Attribute reduction in decision-theoretic rough set model: a further investigation
RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
A new formulation of multi-category decision-theoretic rough sets
RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
Cost-Sensitive classification based on decision-theoretic rough set model
RSKT'12 Proceedings of the 7th international conference on Rough Sets and Knowledge Technology
Decision-Theoretic rough sets with probabilistic distribution
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)
Multi-class decision-theoretic rough sets
International Journal of Approximate Reasoning
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Two stages with bayesian decision procedure are proposed to solve the multiple-category classification problems. The first stage is changing an m-category classification problem into m two-category classification problems, and forming three classes of rules with different actions and decisions by using of decision-theoretic rough sets with bayesian decision procedure. The second stage is choosing the best candidate rules in positive region by using the minimum probability error criterion with bayes decision theory. By considering the levels of tolerance for errors and the costs of actions in real decision procedure, we propose a new approach to deal with the multiple-category classification problems.