Attribute reduction in decision-theoretic rough set models
Information Sciences: an International Journal
Criteria for choosing a rough set model
Computers & Mathematics with Applications
Financial time-series analysis with rough sets
Applied Soft Computing
Learning Optimal Parameters in Decision-Theoretic Rough Sets
RSKT '09 Proceedings of the 4th International Conference on Rough Sets and Knowledge Technology
A Multi-View Decision Model Based on Decision-Theoretic Rough Set
RSKT '09 Proceedings of the 4th International Conference on Rough Sets and Knowledge Technology
Game-theoretic risk analysis in decision-theoretic rough sets
RSKT'08 Proceedings of the 3rd international conference on Rough sets and knowledge technology
Uncertainty measures for rough formulae in rough logic: An axiomatic approach
Computers & Mathematics with Applications
Rough Truth Degrees of Formulas and Approximate Reasoning in Rough Logic
Fundamenta Informaticae
Fundamenta Informaticae - Advances in Rough Set Theory
Rough Truth Degrees of Formulas and Approximate Reasoning in Rough Logic
Fundamenta Informaticae
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One of the challenges a decision maker faces is choos- ing a suitable rough set model to use for data analysis. The traditional algebraic rough set model classifies objects into three regions, namely, the positive, negative, and bound- ary regions. Two different probabilistic models, variable- precision and decision-theoretic, modify these regions via l,u user-defined thresholds and , values from loss func- tions respectively. A decision maker whom uses these mod- els must know what type of decisions can be made within these regions. This will allow him or her to conclude which model is best for their decision needs. We present an out- line that can be used to select a model and better analyze the consequences and outcomes of those decisions.