By virtue of granular computing, attribute reduction of rough sets can effectively perform
information processing. Regarding the decision table with granular structures, the traditional …

The existing research of multi-source information system, pessimistic or optimistic multi-
granulation fusion functions, provided by multi-granulation rough set (MGRS) theory, which …

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 …

In fuzzy neighborhood rough sets (FNRSs), uncertainty measurement performs mainly
classification-hierarchical and multiplication-simple fusion, so the corresponding feature …

Three-way decision theory has emerged as an effective method for attribute reduction when
dealing with vague, uncertain, or imprecise data. However, most existing attribute reduction …

Decision-theoretic rough set model, as a probabilistic generalization of the Pawlak rough set
model, is an effective method for decision making from vague, uncertain or imprecise data …

Neighborhood granulation underlies neighborhood rough sets, and it also induces the basic
classifier of K-nearest neighbor (KNN). Based on neighborhood granulation and its distance …

Rough set theory has been applied extensively to attribute reduction. Classical rough sets
provide a theoretical framework for attribute reduction based on complete data with regular …

Three-way decisions are a fundamental methodology with extensive applications, while
attribute reducts play an important role in data analyses. The combination of both topics has …

Uncertainty measurement is considered as a vital quantitative way for analyzing and mining
potential characteristic features in different types of decision tables. However, considering …