Rough set theory and belief function theory, two popular mathematical frameworks for
uncertainty representation, have been widely applied in different settings and contexts …

Feature selection aims to select a feature subset from an original feature set based on a
certain evaluation criterion. Since feature selection can achieve efficient feature reduction, it …

Attribute reduction serves as a pivotal topic of rough set theory for data analysis. The ideas
of tri-level thinking from three-way decision can shed new light on three-level attribute …

Classical rough set theory is considered a useful tool for dealing with the uncertainty of
categorical data. The major deficiency of this model is that the classical rough set model is …

The knowledge discovery process is as old as Homo sapiens. Until some time ago this
process was solely based on the 'natural personal'computer provided by Mother Nature …

Worldwide, there has been a rapid growth in interest in rough set theory and its applications
in recent years. Evidence of this can be found in the increasing number of high-quality …

Feature selection is a challenging problem in areas such as pattern recognition, machine
learning and data mining. Considering a consistency measure introduced in rough set …

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Feature selection (FS) can be defined as the problem of finding the minimal number of
features from an original set with the minimum information loss. Since FS problems are …

Many real data increase dynamically in size. This phenomenon occurs in several fields
including economics, population studies, and medical research. As an effective and efficient …