Feature selection for multi-label learning based on variable-degree multi-granulation decision-theoretic rough sets

Y Yu, M Wan, J Qian, D Miao, Z Zhang… - International Journal of …, 2024 - Elsevier
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) …

Exploiting feature multi-correlations for multilabel feature selection in robust multi-neighborhood fuzzy β covering space

T Yin, H Chen, J Wan, P Zhang, SJ Horng, T Li - Information Fusion, 2024 - Elsevier
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 …

A survey on multi-label feature selection from perspectives of label fusion

W Qian, J Huang, F Xu, W Shu, W Ding - Information Fusion, 2023 - Elsevier
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 based on correlation label enhancement

Z He, Y Lin, C Wang, L Guo, W Ding - Information Sciences, 2023 - Elsevier
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 …

Ensemble of kernel extreme learning machine based elimination optimization for multi-label classification

Q Zhang, ECC Tsang, Q He, Y Guo - Knowledge-Based Systems, 2023 - Elsevier
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 feature selection via similarity constraints with non-negative matrix factorization

Z He, Y Lin, Z Lin, C Wang - Knowledge-Based Systems, 2024 - Elsevier
Feature selection plays a key role in preprocessing, effectively addressing the curse of
dimensionality in multi-label learning. While current approaches commonly utilize feature or …

Label relaxation and shared information for multi-label feature selection

Y Fan, X Chen, S Luo, P Liu, J Liu, B Chen, J Tang - Information Sciences, 2024 - Elsevier
Due to the rapid growth of labels and high-dimensional data, multi-label feature selection
has attracted increasing attention. However, two common issues are ignored by existing …

Multi-label feature selection based on fuzzy rough sets with metric learning and label enhancement

M Cai, M Yan, P Wang, F Xu - International Journal of Approximate …, 2024 - Elsevier
Multi-label feature selection based on fuzzy rough sets, as a key step of multi-label data
preprocessing, has been widely concerned by scholars in recent years. Most of the existing …

Label distribution feature selection based on hierarchical structure and neighborhood granularity

X Lu, W Qian, S Dai, J Huang - Information Fusion, 2024 - Elsevier
Abstract Label Distribution Learning (LDL) addresses label ambiguity in datasets but
struggles with high-dimensional data due to irrelevant features. Label Distribution Feature …

LEFMIFS: Label enhancement and fuzzy mutual information for robust multilabel feature selection

T Yin, H Chen, Z Yuan, B Sang, SJ Horng, T Li… - … Applications of Artificial …, 2024 - Elsevier
Feature selection is one of the quite important preprocessing steps in multilabel learning.
However, multilabel feature selection is facing big challenges due to high-dimensional and …