Incremental feature selection can improve learning of accumulated data. We focus on
incremental feature selection based on rough sets, which along with their generalizations …

Tolerance rough sets (TRSs) can operate effectively on continuous attributes for pattern
classification. The formulation of a similarity measure plays an important role for TRSs. The …

Similar to feature selection over completely labeled data, the aim of feature selection over
partially labeled data (semi-supervised feature selection) is also to find a feature subset …

The approximation space in rough set theory is important for dealing with uncertainties. As
the information contained in various information systems is constantly updated and changed …

In neighborhood rough set, radius is a key factor. Different radii may generate different
neighborhood relations for discriminating samples. Unfortunately, it is possible that two …

This paper presents a strong data-mining method based on a rough set, which can
simultaneously realize feature selection, classification, and knowledge representation …

As a novel fuzzy covering, fuzzy covering has attracted considerable attention. However, the
traditional fuzzy--covering-based rough set and most of its extended models cannot well fit …

Nowadays the incredibly advanced developments in information technologies have led to
exponential growth in the datasets with respect to both the dimensionality and the sample …

Rough set theory has been proven to be an effective tool to feature subset selection. Current
research usually employ hill-climbing as search strategy to select feature subset. However …

Feature selection is to find relevant features and delete redundant features, which provides
a basis for classification problems. In many real-world applications, mixed-type data …