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 …

Numerous high-dimension multi-label data are produced, leading to the imperative need to
design excellent multi-label feature selection methods. It is of paramount importance to …

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

Abstract Nowadays, Multi-Label Feature Selection (MLFS) attracts more and more attention
to tackle the high-dimensional problem in multi-label data. A key characteristic of existing …

Feature selection is able to select more discriminative features for classification and plays an
important role in multi-label learning to alleviate the effect of the curse of dimensionality …

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 …

Multi-label feature selection plays an important role in pattern recognition, which can
improve multi-label classification performance. In traditional multi-label feature selection …

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 …

High-dimensional multi-labeled data contain instances, where each instance is associated
with a set of class labels and has a large number of noisy and irrelevant features. Feature …

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