Neighborhood rough sets based attribute reduction, as a common dimension reduction
method, has been widely used in machine learning and data mining. Each attribute has the …

The selection of optimal scale has always been the essential problem of multi-scale system.
However, most of the current studies only consider the consistency of the system, and ignore …

As a well-known NP-hard problem, the vertex cover problem has broad applications, which
has aroused the concern of many researchers. In recent years, its related optimization …

Attribute reduction is a fundamental problem in rough set theory and serves as an effective
data reduction technique. The minimal test cost attribute reduction problem poses a more …

Feature selection is a vital preprocessing step in real applications of data mining and
machine learning. With the prevalence of high-dimensional hybrid data sets in real-world …

The problem of finding a minimum weighted vertex cover (MWVC) in a graph is a well-
known combinatorial optimisation problem with important applications. This article …

Attribute reductions are essential pre-processing steps in such as data mining, machine
learning, pattern recognition and many other fields. Moreover, test-cost-sensitive attribute …

Feature selection plays an important role in data mining and machine learning tasks. Rough
set theory has been a prominent tool for this purpose. It characterizes a dataset by using two …

The minimum vertex-cover problem (MVCP) is an NP-complete optimization problem widely
used in areas such as graph theory, social network, security and transportation, etc. Different …

Optimal reduct is one of the challenging problems in rough set theory, and most of the
existing algorithms cannot achieve the optimal reduct on high dimensional data sets. To …