With the advancement of data technology and storage services, the scale and complexity of
data are rapidly growing. Consequently, promptly analyzing data and deriving precise …

Neighborhood rough set is an important model in feature selection. However, it only
determines the granularity of the neighborhood from a feature perspective, while ignoring …

Attribute reduction is capable of reducing the dimensionality of data and improving the
performance of data mining. As a reasonable representative of relationships between …

Graph-based unsupervised feature selection methods have successfully processed high-
dimensional data since they can effectively preserve data structure information. However …

Classification as an essential part of Machine Learning and Data Mining has significant
roles in engineering, medicine, agriculture, military, etc. With the evolution of data collection …

Neighborhood rough sets-based methods have been widely used for feature selection.
However, the existing methods have some problems in neighborhood construction, such as …

In an effort to enhance the precision of wind power prediction, this study proposes a wind
power prediction model with a secondary-weighted attention mechanism, which is based on …

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

Fuzzy-rough Sets (FRS) provide an outstanding theoretical tool for Feature Selection (FS).
Whilst promising, the FRS model is sensitive to noisy information and ineffectively applicable …

Dimensionality Reduction plays a pivotal role in improving feature learning accuracy and
reducing training time by eliminating redundant features, noise, and irrelevant data …