Learn 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)
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)
—The decision-theoretic rough set model (DTRSM) was proposed two decades ago. In this paper, the development of DTRSM, including the theories and the potential applications, are reviewed. With respect to the two semantic issues of DTRSM, three-way decision procedure, attribute reduction methods and potential applications of DTRSM are examined. This paper(More)
A novel interval set approach is proposed in this paper to induce classification rules from incomplete information table, in which an interval-set-based model to represent the uncertain concepts is presented. The extensions of the concepts in incomplete information table are represented by interval sets, which regulate the upper and lower bounds of the(More)