High-dimensional data analysis is a challenge for researchers and engineers in the fields of
machine learning and data mining. Feature selection provides an effective way to solve this …

Feature selection, which plays an important role in high-dimensional data analysis, is
drawing increasing attention recently. Finding the most relevant and important features for …

Feature selection based on neighborhood rough sets (NRSs) has become a popular area of
research in data mining. However, the limitation that NRSs inherently ignore the differences …

Feature selection is a highly regarded research area in the field of data mining, as it
significantly enhances the efficiency and performance of high-dimensional data analysis by …

Semi-supervised feature selection alleviates the annotation burden of supervised feature
learning by exploiting data under a handful of supervision information. The mainstream …

With the speedy development of network technology, diverse data increase by hundreds of
millions per hour, causing increasing pressure on the acquisition of data labels. Semi …

To remove redundant features and avoid the curse of dimensionality, the most important
features should be selected for downstream tasks, including semi-supervised learning …

Recently, multilabel classification is of increasing interest in machine learning and artificial
intelligence. However, the distances of samples in most Relief methods easily result in …

Partial labeled data is ubiquitous in the big data era. Selecting informative features, and
avoiding redundant and noise features is an important task for constructing robust learning …

Establishing an accurate quality prediction model is an essential prerequisite for quality
management in diesel engine manufacturing factories. However, the large production scale …