Through abstracting commonness from the existing heuristic algorithms, control strategies
bring us higher level understandings of building reducts in rough set theory. To further …

An information system is an important model in the field of artificial intelligence and its
information structures mean a mathematical structure of the family of information granules …

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 …

Effective feature selection determines the efficiency and accuracy of a learning process,
which is essential in human activity recognition. In existing works, for simplification …

Feature selection plays an important role in the classification problems associated with
expert and intelligent systems. The central idea behind feature selection is to identify …

Rough sets and formal concept analysis are two complementary tools during the process of
data analysis. Two-universe rough set model is one of generalization of the classical rough …

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 …

With the volume of data growing at an unprecedented rate, knowledge acquisition for big
data has become a new challenge. To address this issue, information granules in different …

In the rough-set field, the objective of attribute reduction is to regulate the variations of
measures by reducing redundant data attributes. However, most of the previous concepts of …

In rough set theory, forward heuristic algorithm selects the most important attribute in the
process of attribute reduction until the given constraint is satisfied. However, the attributes …