Turnitin
降AI改写
早检测系统
早降重系统
Turnitin-UK版
万方检测-期刊版
维普编辑部版
Grammarly检测
Paperpass检测
checkpass检测
PaperYY检测
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) …
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
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 …
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 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 …
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 …
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 …
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 …
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 …
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 …
struggles with high-dimensional data due to irrelevant features. Label Distribution Feature …
LEFMIFS: Label enhancement and fuzzy mutual information for robust multilabel feature selection
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 …
However, multilabel feature selection is facing big challenges due to high-dimensional and …