Feature selection is a meaningful aspect of data mining that aims to select more relevant
data features and provide more concise and explicit data descriptions. It is beneficial for …

Different from the traditional two-way decision paradigm, three-way decisions (3WD) have
been extensively explored in addressing realistic multi-attribute decision making (MADM) by …

Information fusion is capable of fusing and transforming information originated from multiple
sources into an integrated representation. As an important representative of information …

By means of a fuzzy coimplication operator J and a triangular conorm S, we set forth two
pairs of (J, S)-fuzzy rough set models, which are generalizations of fuzzy rough sets. Then …

The concept of q-rung orthopair fuzzy sets (q-ROFSs) severs as an extended form of
orthopair fuzzy sets, which excels in flexibly depicting imprecise information existed in …

Attribute reduction of rough set theory underlies knowledge acquisition and has two
hierarchical types (classification-based and class-specific attribute reducts) and two …

Multi-attribute decision-making (MADM) can be regarded as a process of selecting the
optimal one from all alternatives. Traditional MADM problems with fuzzy information are …

Incremental learning strategy based feature selection approaches can improve the efficiency
of reduction algorithm used for datasets with dynamic characteristic, which has attracted …

Interval-valued information systems provide rich semantic interpretation and greater
flexibility compared to real-valued information systems. Meanwhile, incomplete interval …

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