Neighborhood rough set based online streaming feature selection methods have aroused
wide concern in recent years and played a vital role in processing high-dimensional data …

Multi-label feature selection has grabbed intensive attention in many big data applications.
However, traditional multi-label feature selection methods generally ignore a real-world …

Multi-label feature selection is an indispensable technology in multi-semantic high-
dimensional data preprocessing, which has been brought into focus in recent years …

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 …

Feature selection is an essential pre-processing part in multi-label learning. Multi-label
learning is usually used to deal with many complicated tasks, in which each sample is …

In multilabel learning, the curse of dimensionality is one of major challenges. Existing single-
label feature selection methods cannot be directly applied to multilabel data, and multilabel …

In recent years, feature selection for multilabel classification has attracted attention in
machine learning and data mining. However, some feature selection methods ignore the …

Feature selection attempts to capture the more discriminative features and plays a significant
role in multilabel learning. As an efficient mathematical tool to handle incomplete and …

Feature selection for multi-label classification tasks has attracted attention from the machine
learning domain. The current algorithms transform a multi-label learning task to several …

Recently, multilabel classification has generated considerable research interest. However,
the high dimensionality of multilabel data incurs high costs; moreover, in many real …