Label distribution feature selection based on label-specific features DOI
Wenhao Shu, Qiang Xia, Wenbin Qian

и другие.

Applied Intelligence, Год журнала: 2024, Номер 54(19), С. 9195 - 9212

Опубликована: Июль 11, 2024

Язык: Английский

Label-specific disentanglement and correlation-guided fusion for multi-label classification DOI
Yang Li, Kun Wang,

Leting Tan

и другие.

Knowledge and Information Systems, Год журнала: 2025, Номер unknown

Опубликована: Фев. 21, 2025

Язык: Английский

Процитировано

0
Improving Human Brucellosis Susceptibility Mapping Using Effective and Simultaneously Metaheuristic-based Feature Selection and Hyperparameter Tuning DOI
Iman Zandi,

Ali Jafari,

Ali Asghar Alesheikh

и другие.

Acta Tropica, Год журнала: 2025, Номер unknown, С. 107657 - 107657

Опубликована: Май 1, 2025

Язык: Английский

Процитировано

0
Adaptive label secondary reconstruction for missing multi-label learning DOI

Zhi Qin,

Hongmei Chen,

Tengyu Yin

и другие.

Knowledge-Based Systems, Год журнала: 2024, Номер 299, С. 112019 - 112019

Опубликована: Май 31, 2024

Язык: Английский

Процитировано

2
A novel multi-label feature selection method based on knowledge consistency-independence index DOI
Xiangbin Liu,

Heming Zheng,

Wenxiang Chen

и другие.

Information Sciences, Год журнала: 2024, Номер 677, С. 120870 - 120870

Опубликована: Июнь 5, 2024

Язык: Английский

Процитировано

2
Arctic tern-optimized weighted feature regression system for predicting bridge scour depth DOI Creative Commons
Jui‐Sheng Chou,

Asmare Molla

Engineering Applications of Computational Fluid Mechanics, Год журнала: 2024, Номер 18(1)

Опубликована: Июль 8, 2024

This paper presents a pioneering artificial intelligence (AI) solution – the Arctic Tern-Optimized Weighted Feature Least Squares Support Vector Regression (ATO-WFLSSVR) system to aid civil engineers in accurately predicting scour depth at bridges. prediction amalgamates strengths of hybrid models by uniting metaheuristic optimization algorithm with weighted features and least squares support vector regression (WFLSSVR). The concurrently optimizes all hyperparameters constituent WFLSSVR models, resulting highly effective system. Validation involves comprehensive assessment using two case studies, which include datasets depths across various complexities pier foundation types. Comparative analyses against single AI conventional ensemble techniques, empirical methods demonstrate that ATO-WFLSSVR's reliability outperforms others performance evaluation metrics. Specifically, for field dataset, ATO-WFLSSVR achieves MAPE R values 20.92% 0.9435, respectively, data complex foundations, it records 6.49% 0.9384, respectively. automated predictive analytics underscore robustness, efficiency, stability compared existing methods. study's notable contributions development an innovative named Terns Optimizer (ATO), proficiency solving high-dimensional problems, creation user-friendly graphical interface system, promising tool estimate Further testing diverse encompassing more scenarios are recommended. source code this study currently accessible https://www.researchgate.net/profile/Jui-Sheng-Chou/publications.

Язык: Английский

Процитировано

2
Multi-label feature selection based on nonlinear mapping DOI
Yan Wang, Changzhong Wang, Tingquan Deng

и другие.

Information Sciences, Год журнала: 2024, Номер 680, С. 121168 - 121168

Опубликована: Окт. 1, 2024

Язык: Английский

Процитировано

2
Multi-label feature selection based on minimizing feature redundancy of mutual information DOI

Gaozhi Zhou,

Runxin Li,

Zhenhong Shang

и другие.

Neurocomputing, Год журнала: 2024, Номер 607, С. 128392 - 128392

Опубликована: Авг. 14, 2024

Язык: Английский

Процитировано

1
AME-LSIFT: Attention-Aware Multi-Label Ensemble with Label Subset-SpecIfic FeaTures DOI
Xinying Zhang, Ran Wang, Shuyue Chen

и другие.

IEEE Transactions on Knowledge and Data Engineering, Год журнала: 2024, Номер 36(12), С. 7627 - 7642

Опубликована: Авг. 22, 2024

Язык: Английский

Процитировано

1
Weakly-supervised label distribution feature selection via label-specific features and label correlation DOI
Wenhao Shu, Jiayu Hu, Wenbin Qian

и другие.

International Journal of Machine Learning and Cybernetics, Год журнала: 2024, Номер unknown

Опубликована: Сен. 24, 2024

Язык: Английский

Процитировано

1
Weakly-supervised label distribution feature selection via label-specific features and label correlation DOI Creative Commons
Wenhao Shu, Jiayu Hu, Wenbin Qian

и другие.

Research Square (Research Square), Год журнала: 2024, Номер unknown

Опубликована: Янв. 30, 2024

Abstract Label Distribution Learning (LDL) stands out as an innovative method to address the challenges posed by label ambiguity. Current LDL algorithms are predominantly designed for datasets with comprehensive supervised information. However, in real-world scenarios, it’s common encounter partial missing labels within space. This phenomenon disrupts structure and correlation between labels, posing a challenge precise design of learning algorithms. Furthermore, distribution learning, it also faces effect high feature dimensionality. To tackle this, adopting pre-processing methods like selection becomes crucial, aiming trim down data Motivated weakly-supervised algorithm based on is proposed this paper. First, handle data, two-stage incomplete (IncomLDLTS) recover exploiting label-independent prediction label-specific features proposed. Second, minimum (MCLFS) enhance performance complete designed, which employs interaction information metric explore obtain label, then about each redundancy captured. Finally, six representative evaluation metrics, our experiments across 14 affirm effectiveness approach, not only restoring but choosing essential features, leading enhanced classification accuracy.

Язык: Английский

Процитировано

0