| Class-specific mutual information variation for feature selection W Gao, L Hu, P Zhang Pattern Recognition 79, 328-339, 2018 | 149 | 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 | 140 | 2018 |
| Distinguishing two types of labels for multi-label feature selection P Zhang, G Liu, W Gao Pattern Recognition 95, 72-82, 2019 | 127 | 2019 |
| Feature selection considering the composition of feature relevancy W Gao, L Hu, P Zhang, J He Pattern Recognition Letters 112, 70-74, 2018 | 93 | 2018 |
| 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 | 72 | 2018 |
| Multi-label feature selection with shared common mode L Hu, Y Li, W Gao, P Zhang, J Hu Pattern Recognition 104, 107344, 2020 | 70 | 2020 |
| 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 | 69 | 2022 |
| Robust multi-label feature selection with dual-graph regularization J Hu, Y Li, W Gao, P Zhang Knowledge-Based Systems 203, 106126, 2020 | 63 | 2020 |
| Feature redundancy term variation for mutual information-based feature selection W Gao, L Hu, P Zhang Applied Intelligence 50, 1272-1288, 2020 | 60 | 2020 |
| Multi-label feature selection considering label supplementation P Zhang, G Liu, W Gao, J Song Pattern recognition 120, 108137, 2021 | 42 | 2021 |
| MFSJMI: Multi-label feature selection considering join mutual information and interaction weight P Zhang, G Liu, J Song Pattern Recognition 138, 109378, 2023 | 33 | 2023 |
| 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 | 33 | 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 | 33 | 2021 |
| Feature selection considering uncertainty change ratio of the class label P Zhang, W Gao Applied Soft Computing 95, 106537, 2020 | 27 | 2020 |
| Feature selection considering weighted relevancy P Zhang, W Gao, G Liu Applied Intelligence 48, 4615-4625, 2018 | 26 | 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 | 20 | 2020 |
| Preserving similarity and staring decisis for feature selection W Gao, L Hu, Y Li, P Zhang IEEE Transactions on Artificial Intelligence 2 (6), 584-593, 2021 | 16 | 2021 |
| Feature redundancy based on interaction information for multi-label feature selection W Gao, J Hu, Y Li, P Zhang IEEE Access 8, 146050-146064, 2020 | 16 | 2020 |
| A conditional-weight joint relevance metric for feature relevancy term P Zhang, W Gao, J Hu, Y Li Engineering Applications of Artificial Intelligence 106, 104481, 2021 | 15 | 2021 |
| Feature relevance term variation for multi-label feature selection P Zhang, W Gao Applied Intelligence 51, 5095-5110, 2021 | 13 | 2021 |
