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 …

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 …

Recent years has witnessed urgent needs for addressing the curse of dimensionality
regarding multi-label data, which attracts wide attention for feature selection. Feature …

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 approaches aim to select a set of prominent features that best describe the
data to improve the efficiency without degrading the performance of the model. In many real …

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 …

Information theoretical-based methods have attracted a great attention in recent years and
gained promising results for multilabel feature selection (MLFS). Nevertheless, most of the …

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 …

High-dimensional multilabel data have increasingly emerged in many application areas,
suffering from two noteworthy issues: instances with high-dimensional features and large …