In many real-world application domains, eg, text categorization and image annotation,
objects naturally belong to more than one class label, giving rise to the multi-label learning …

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 …

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

To date, multilabel learning has garnered attention increased from scholars and has a
significant effect on practical applications; however, most feature selection models with …

Feature selection, a meaningful preprocessing technique in machine learning, plays a key
role in multi-label learning to select more discriminative features. Recently, multi-label …

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 …

One of the biggest dangers to society today is terrorism, where attacks have become one of
the most significant risks to international peace and national security. Big data, information …

As combinations of neighborhood rough sets and multigranulation rough sets (MRSs),
optimistic and pessimistic neighborhood MRSs can handle complex information systems …

Multi-label feature selection (MFS) has gained in importance, and it is today confronted with
the current need to process multi-semantic high-dimensional data. Recent studies usually …

Due to the diversity and complexity of the actual data distribution, the traditional
neighborhood classifier (NEC) is weak in adapting to the global data and has low utilization …