Feature selection can identify essential features and reduce the dimensionality of features,
improving the classification ability of a learning model. In this study, we consider data with a …

Rough set theory, proposed in 1982 by Zdzislaw Pawlak, is in constant development. It is
concerned with the classification and analysis of imprecise or uncertain information and …

A framework of cost-sensitive classification based on decision-theoretic rough set model is
proposed to determine the local minimum total cost classification and the local optimal test …

The Evidential K-Nearest Neighbor (EK-NN) classification rule provides a global treatment of
imperfect knowledge in class labels, but still suffers from the curse of dimensionality as well …

Feature selection plays an important role in pattern recognition and machine learning.
Confronted with high dimensional data in many data analysis tasks, feature selection …

Dataset dimensionality is one of the primary impediments to data analysis in areas such as
pattern recognition, data mining, and decision support. A feature subset that possesses the …

The classification of high-dimensional tasks remains a significant challenge for machine
learning algorithms. Feature selection is considered to be an indispensable preprocessing …

As we all know, the multi-label learning is faced with the" dimension disaster", and the label
distribution learning is actually faced with the same problem. Therefore, it is necessary to …

As a useful extension of rough sets, multigranulation rough sets (MGRSs) can be used to
deal with a variety of complex data. Numerous significant advances have been achieved by …