Multi-label learning is a rapidly growing research area that aims to predict multiple labels
from a single input data point. In the era of big data, tasks involving multi-label classification …

Multi-label learning (MLL) suffers from the high-dimensional feature space teeming with
irrelevant and redundant features. To tackle this, several multi-label feature selection (MLFS) …

Feature selection (FS) has garnered significant attention because of its pivotal role in
enhancing the efficiency and effectiveness of various machine learning and data mining …

Feature selection is a key technique to tackle the curse of dimensionality in multi-label
learning. Lots of embedded multi-label feature selection methods have been developed …

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

Over the past decade, the utilization of machine learning (ML) models for recognizing hand
gestures from surface electromyography (sEMG) signals has been in demand for the control …

Partial multilabel learning (PML) addresses the issue of noisy supervision, which contains
an overcomplete set of candidate labels for each instance with only a valid subset of training …

Multi-source incomplete mixed data abound in real life, like medical data, biological data,
remote sensing data, military data, etc. However, some of these sources are of less …

In multi-label feature selection (MLFS), pseudo-label learning techniques are often
employed to mitigate the issue that the binary nature of ground-truth labels is incompatible …

Partial label learning (PLL) tackles scenarios where the unique ground-truth label of each
sample is concealed within a candidate label set. Dimensionality reduction, considering …