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 …

Multilabel data contains rich label semantic information, and its data structure conforms to
the cognitive laws of the actual world. However, these data usually involve many irrelevant …

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 …

Feature selection is an effective data preprocessing technique that can effectively alleviate
the curse of dimensionality in multi-label learning. The technique selects a subset of features …

Abstract Label Distribution Learning (LDL) addresses label ambiguity in datasets but
struggles with high-dimensional data due to irrelevant features. Label Distribution Feature …

The multi-label classification task is more challenging than the degenerated case of single-
label classification due to diversified uncertainty. Uncertainty in multi-label classification not …

Label distribution learning (LDL) is an emerging framework in machine learning. Fuzzy
mutual information is mutual information under a fuzzy environment and plays an important …

Multi-label feature selection plays an increasingly important role in alleviating the high
dimensionality of multi-label learning tasks. Most extant methods posit that the learning task …

Feature selection is an effective method to simplify data analysis and obtain key features,
which improves the accuracy and generalization ability of classifiers. Neighbourhood rough …

Multi-label feature selection is one of the key steps in dealing with multi-label classification
problems in high-dimensional data. In this step, label enhancement techniques play an …