| Class-specific mutual information variation for feature selection W Gao, L Hu, P Zhang Pattern Recognition 79, 328-339, 2018 | 161 | 2018 |
| Feature selection considering two types of feature relevancy and feature interdependency L Hu, W Gao, K Zhao, P Zhang, F Wang Expert Systems with Applications 93, 423-434, 2018 | 147 | 2018 |
| Distinguishing two types of labels for multi-label feature selection P Zhang, G Liu, W Gao Pattern recognition 95, 72-82, 2019 | 133 | 2019 |
| Feature selection considering the composition of feature relevancy W Gao, L Hu, P Zhang, J He Pattern Recognition Letters 112, 70-74, 2018 | 103 | 2018 |
| Feature-specific mutual information variation for multi-label feature selection L Hu, L Gao, Y Li, P Zhang, W Gao Information Sciences 593, 449-471, 2022 | 83 | 2022 |
| Feature selection by integrating two groups of feature evaluation criteria W Gao, L Hu, P Zhang, F Wang Expert Systems with Applications 110, 11-19, 2018 | 79 | 2018 |
| Multi-label feature selection with shared common mode L Hu, Y Li, W Gao, P Zhang, J Hu Pattern Recognition 104, 107344, 2020 | 77 | 2020 |
| Robust multi-label feature selection with dual-graph regularization J Hu, Y Li, W Gao, P Zhang Knowledge-Based Systems 203, 106126, 2020 | 74 | 2020 |
| Multilabel feature selection with constrained latent structure shared term W Gao, Y Li, L Hu IEEE Transactions on Neural Networks and Learning Systems 34 (3), 1253-1262, 2023 | 71 | 2023 |
| Multi-label feature selection via robust flexible sparse regularization Y Li, L Hu, W Gao Pattern Recognition 134, 109074, 2023 | 70 | 2023 |
| Feature redundancy term variation for mutual information-based feature selection W Gao, L Hu, P Zhang Applied Intelligence 50 (4), 1272-1288, 2020 | 67 | 2020 |
| Multi-label feature selection considering label supplementation P Zhang, G Liu, W Gao, J Song Pattern recognition 120, 108137, 2021 | 47 | 2021 |
| A unified low-order information-theoretic feature selection framework for multi-label learning W Gao, P Hao, Y Wu, P Zhang Pattern Recognition 134, 109111, 2023 | 41 | 2023 |
| Multi-label feature selection based on the division of label topics P Zhang, W Gao, J Hu, Y Li Information Sciences 553, 129-153, 2021 | 36 | 2021 |
| Dynamic subspace dual-graph regularized multi-label feature selection J Hu, Y Li, G Xu, W Gao Neurocomputing 467, 184-196, 2022 | 35 | 2022 |
| Robust sparse and low-redundancy multi-label feature selection with dynamic local and global structure preservation Y Li, L Hu, W Gao Pattern Recognition 134, 109120, 2023 | 34 | 2023 |
| Feature selection considering uncertainty change ratio of the class label P Zhang, W Gao Applied Soft Computing 95, 106537, 2020 | 32 | 2020 |
| Label correlations variation for robust multi-label feature selection Y Li, L Hu, W Gao Information Sciences 609, 1075-1097, 2022 | 28 | 2022 |
| Feature selection considering weighted relevancy P Zhang, W Gao, G Liu Applied Intelligence 48, 4615-4625, 2018 | 27 | 2018 |
| Multi-label feature selection based on high-order label correlation assumption P Zhang, W Gao, J Hu, Y Li Entropy 22 (7), 797, 2020 | 23 | 2020 |
Ping ZhangHebei University of TechnologyEmail được xác minh tại hebut.edu.cn
