Learn More
Rough set theory has witnessed great success in data mining and knowledge discovery, which provides a good support for decision making on a certain data. However, a practical decision problem always shows diversity under the same circumstance according to different personality of the decision makers. A simplex decision model can not provide a full(More)
For most attribute reduction in Pawlak rough set model (PRS), monotonicity is a basic property for the quantitative measure of an attribute set. Based on the monotonicity, a series of attribute reductions in Pawlak rough set model such as positive-region-preserved reductions and condition entropy-preserved reductions are defined and the corresponding(More)
By considering the levels of tolerance for errors and the cost of actions in real decision procedure, a new two-stage approach is proposed to solve the multiple-category classification problems with Decision-Theoretic Rough Sets (DTRS). The first stage is to change an m-category classification problem (m > 2) into an m two-category classification problem,(More)
The monotonicity of positive region in PRS (Pawlak Rough Set) and DTRS (Decision-Theoretic Rough Set) are comparatively discussed in this paper. Theoretic analysis shows that the positive region in DTRS model may expand with the decrease of the attributes, which is essentially different from that of PRS model and leads to a new definition of attribute(More)