In the era of open machine learning, a kind of data is accompanied by a hierarchical
structure between classes in the label space and the increasing number of features …

Feature selection is an effective data pre-processing technique that aims to select useful
features. This technique has been widely applied in machine learning and data mining to …

Feature selection using variable precision neighborhood rough sets (VPNRS) has garnered
considerable attention in data mining and knowledge discovery. Nevertheless, the positive …

Multi-label learning, like other machine learning methods, suffers from dimensionality
disaster. However, due to the limitations of multi-label dimensionality reduction frameworks …

As financial markets grow increasingly complex and dynamic, accurately predicting stock
price trends becomes crucial for investors and financial analysts. Effectively identifying and …

Neighborhood rough set has become a mature means of attribute reduction for simplifying
modeling and knowledge discovery in continuous and mixed data. It principally generates …

In the realm of machine learning, feature selection emerges as a prevalent data
preprocessing technique, playing a crucial role in enhancing model performance across …

This study proposed an innovative fuzzy rough set model to address multi-attribute decision-
making problems. Initially, we introduced a novel model of multi-granularity variable …

As a very prominent research application of the theory of rough sets, attribute reduction
technique has made significant strides in a lot of fields, including decision making, granular …

The recently developed transformer has been largely explored in the research field of
computer vision and especially improve the performance of single object tracking. However …