Feature selection is a challenging problem in many areas such as pattern recognition,
machine learning and data mining. Rough set theory, as a valid soft computing tool to …

Some disadvantages should be discussed deeply for the current reduction algorithms. To
eliminate these limitations of classical algorithms based on positive region and conditional …

In neighborhood rough set theory, traditional heuristic algorithm for computing reducts does
not take the stability of the selected attributes into account, it follows that the performances of …

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 …

Attribute reduction is one of the most important problems in rough set theory. Conventional
attribute reduction algorithms are based on minimal errors in seen objects, namely empirical …

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 …

The variable precision rough set model resists noise in data by introducing a parameter to
relax the strict inclusion in approximations of the classical rough set model, and attribute …

Attribute reduction is a key issue in rough set theory which is widely used to handle
uncertain knowledge. However, most existing attribute reduction approaches focus on cost …

The theory of rough sets provides a method to construct three types of classification rules,
leading to three-way decisions. From such a point of view, we introduce the concept of cost …

The comparable property (or inclusion property) is the basic property of rough set theory.
Unfortunately, the well-known granular variable precision fuzzy rough set does not satisfy …