As an extension of Pawlak rough set model, decision-theoretic rough set model (DTRS)
adopts the Bayesian decision theory to compute the required thresholds in probabilistic …

Decision-theoretic rough set model can derive several probabilistic rough set models by
providing proper cost functions. Learning cost functions from data automatically is the key to …

Decision-theoretic rough set model interprets the parameters of existing probabilistic rough
sets by introducing Bayesian theory to minimize the risks of classification. It is a …

This paper considers an optimization viewpoint of decision-theoretic rough set model. An
optimization problem is proposed by considering the minimization of the decision cost …

Neighborhood rough sets provide important insights into dealing with numerical data.
Neighborhood radius, a key factor that affects data uncertainty, is uniformly given in most of …

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 …

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 …

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 …

In classical rough set models, attribute reduction generally keeps the positive or non-
negative regions unchanged, as these regions do not decrease with the addition of …

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 …