In real application domains, acquiring fine-grained data has a higher cost than coarse-
grained data. To achieve the best results at the lowest cost, it is necessary to select an …

Cost is an important issue in real world data mining. In rough set community, test cost and
decision cost are two popular costs which are addressed by many researchers. In recent …

In real applications of data mining, machine learning and granular computing, measurement
errors, test costs and misclassification costs often occur. Furthermore, the test cost of a …

Neighborhood rough set model is considered as one of the effective granular computing
models in dealing with numerical data. This model is now widely discussed in feature …

Granular Computing (GrC), as a framework for data/information processing, is an umbrella
term that encompasses a breadth of Soft Computing methods. Focusing on Machine …

Multigranulation rough set is an expansion of the classical rough set by using multiple
granular structures. Presently, three important multigranulation rough sets have been …

In rough set based Granular Computing, neighborhood relation has been widely accepted
as one of the most popular approaches for realizing information granulation. Such approach …

Multigranulation rough set theory is a desirable direction in the field of rough set, in which
upper and lower approximations are approximated by multiple granular structures. However …

Cost sensitivity is an important problem, which has been addressed by many researchers
around the world. As far as cost sensitivity in the rough set theory is concerned, two types of …

Abstract The Dominance-based Rough Set Approach (DRSA) is an extension of RST, which
utilizes the dominance relation in attributes. However, traditional DRSA-based methods do …