Abstract Recent advancements in Information Technology (IT) have engendered the rapid
production of big data, as enormous volumes of data with high dimensional features grow …

In many real-world multi-label applications, the content of multi-label data is usually
characterized by high dimensional features, which contains complex correlation information …

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 captures a reliable and informative feature subset from high-
dimensional multi-label data, which plays an important role in pattern recognition. In …

Multi-label feature selection is an efficient technique to deal with the high dimensional multi-
label data by selecting the optimal feature subset. Existing researches have demonstrated …

The task of multi-label feature selection (MLFS) is to reduce redundant information and
generate the optimal feature subset from the original multi-label data. A variety of MLFS …

Feature selection plays a crucial role in machine learning and data mining, and improves
the performance of learning models by selecting a distinguishing feature subset and …

Multi-label feature selection, which addresses the challenge of high dimensionality in multi-
label learning, has wide applicability in pattern recognition, machine learning, and related …

The approximation of low-order information-theoretic terms for feature selection approaches
has achieved success in addressing high-dimensional multi-label data. However, three …