Multi-label feature selection with missing features by tolerance implication granularity information and symmetric coupled discriminant weight DOI
Jianhua Dai,

Jie Wang

Pattern Recognition, Год журнала: 2025, Номер unknown, С. 111365 - 111365

Опубликована: Янв. 1, 2025

Язык: Английский

Semi-supervised imbalanced multi-label classification with label propagation DOI
Guodong Du, Jia Zhang, Ning Zhang

и другие.

Pattern Recognition, Год журнала: 2024, Номер 150, С. 110358 - 110358

Опубликована: Фев. 21, 2024

Язык: Английский

Процитировано

20

Cross-to-merge training with class balance strategy for learning with noisy labels DOI Creative Commons
Qian Zhang, Yi Zhu, Ming Yang

и другие.

Expert Systems with Applications, Год журнала: 2024, Номер 249, С. 123846 - 123846

Опубликована: Март 29, 2024

The collection of large-scale datasets inevitably introduces noisy labels, leading to a substantial degradation in the performance deep neural networks (DNNs). Although sample selection is mainstream method field learning with which aims mitigate impact labels during model training, testing these methods exhibits significant fluctuations across different noise rates and types. In this paper, we propose Cross-to-Merge Training (C2MT), novel framework that insensitive prior information progress, enhancing robustness. practical implementation, using cross-divided training data, two are cross-trained co-teaching strategy for several local rounds, subsequently merged into unified by performing federated averages on parameters models periodically. Additionally, introduce new class balance strategy, named Median Balance Strategy (MBS), cross-dividing process, evenly divides data labeled subset an unlabeled based estimated loss distribution characteristics. Extensive experimental results both synthetic real-world demonstrate effectiveness C2MT. Code will be available at: https://github.com/LanXiaoPang613/C2MT.

Язык: Английский

Процитировано

20

A survey on multi-label feature selection from perspectives of label fusion DOI
Wenbin Qian, Jintao Huang,

Fankang Xu

и другие.

Information Fusion, Год журнала: 2023, Номер 100, С. 101948 - 101948

Опубликована: Авг. 2, 2023

Язык: Английский

Процитировано

38

Multi-label feature selection by strongly relevant label gain and label mutual aid DOI
Jianhua Dai, Weiyi Huang, Chucai Zhang

и другие.

Pattern Recognition, Год журнала: 2023, Номер 145, С. 109945 - 109945

Опубликована: Сен. 9, 2023

Язык: Английский

Процитировано

38

Ensemble of kernel extreme learning machine based elimination optimization for multi-label classification DOI
Qingshuo Zhang, Eric C.C. Tsang, Qiang He

и другие.

Knowledge-Based Systems, Год журнала: 2023, Номер 278, С. 110817 - 110817

Опубликована: Июль 24, 2023

Язык: Английский

Процитировано

24

Multilabel Feature Selection via Shared Latent Sublabel Structure and Simultaneous Orthogonal Basis Clustering DOI
Ronghua Shang, Jingyu Zhong, Weitong Zhang

и другие.

IEEE Transactions on Neural Networks and Learning Systems, Год журнала: 2024, Номер 36(3), С. 5288 - 5303

Опубликована: Апрель 24, 2024

Multilabel feature selection solves the dimension distress of high-dimensional multilabel data by selecting optimal subset features. Noisy and incomplete labels raw hinder acquisition label-guided information. In existing approaches, mapping label space to a low-dimensional latent semantic decomposition mitigate noise is considered an effective strategy. However, decomposed contains redundant information, which misleads capture potential relevance. To eliminate effect information on extraction correlations, novel method named SLOFS via shared sublabel structure simultaneous orthogonal basis clustering for proposed. First, base (LOBSS) term engineered guide construction redundancy-free separated center structure. The LOBSS simultaneously retains Moreover, relevance nonredundant sublabels are fully explored. introduction graph regularization ensures structural consistency in sublabels, thus helping process. employs dynamic obtain high-quality uses constrain correlations projections. Finally, convergence provable optimization scheme proposed solve method. experimental studies 18 datasets demonstrate that presented performs consistently better than previous methods.

Язык: Английский

Процитировано

15

Multi-label feature selection via similarity constraints with non-negative matrix factorization DOI
Zhuoxin He, Yaojin Lin, Zilong Lin

и другие.

Knowledge-Based Systems, Год журнала: 2024, Номер 297, С. 111948 - 111948

Опубликована: Май 18, 2024

Язык: Английский

Процитировано

12

Multi-label Feature selection with adaptive graph learning and label information enhancement DOI

Zhi Qin,

Hongmei Chen,

Yong Mi

и другие.

Knowledge-Based Systems, Год журнала: 2024, Номер 285, С. 111363 - 111363

Опубликована: Янв. 3, 2024

Язык: Английский

Процитировано

11

Correlation concept-cognitive learning model for multi-label classification DOI
Jiaming Wu, Eric C.C. Tsang, Weihua Xu

и другие.

Knowledge-Based Systems, Год журнала: 2024, Номер 290, С. 111566 - 111566

Опубликована: Фев. 24, 2024

Язык: Английский

Процитировано

10

Multi-label feature selection based on stable label relevance and label-specific features DOI
Yong Yang, Hongmei Chen,

Yong Mi

и другие.

Information Sciences, Год журнала: 2023, Номер 648, С. 119525 - 119525

Опубликована: Авг. 16, 2023

Язык: Английский

Процитировано

23