With the rapid advancement of big data technology, high-dimensional datasets comprising
multi-label data have become prevalent in various fields. However, these datasets often …

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 learning is a class of machine learning algorithms that study the classification
problem of data associated with multiple labels simultaneously. Ensemble-based method is …

Multi-label learning tasks usually encounter the problem of the class-imbalance, where
samples and their corresponding labels are non-uniformly distributed over multi-label data …

Multi-label feature selection can efficiently handle large amounts of multi-label data.
However, two pressing issues remain in sparse learning for multi-label data. First, many …

Feature selection can alleviate the problem of the curse of dimensionality by selecting more
discriminative features, which plays an important role in multi-label learning. Recently …

Recently, some existing feature selection approaches neglect the correlation among labels,
and almost manifold-based multilabel learning models do not considered the relationship …

Multi-view and multi-label data offer a comprehensive perspective for learning models, but
dimensionality poses a challenge for feature selection. Existing methods based on …

As in single-label learning, multi-label learning (MLL) also suffers from the problem of “the
curse of dimensionality” due to the redundancy of features in the original data. To address …

Multi-label classification (MLC) deals with instances associated with multiple labels
simultaneously and often includes high dimensional data with noisy, irrelevant, and …