| Manifold regularized discriminative feature selection for multi-label learning J Zhang, Z Luo, C Li, C Zhou, S Li Pattern Recognition 95, 136-150, 2019 | 230 | 2019 |
| An effective collaborative filtering algorithm based on user preference clustering J Zhang, Y Lin, M Lin, J Liu Applied Intelligence 45, 230-240, 2016 | 121 | 2016 |
| Multi-label learning with label-specific features by resolving label correlations J Zhang, C Li, D Cao, Y Lin, S Su, L Dai, S Li Knowledge-Based Systems 159, 148-157, 2018 | 97 | 2018 |
| Fast multilabel feature selection via global relevance and redundancy optimization J Zhang, Y Lin, M Jiang, S Li, Y Tang, J Long, J Weng, KC Tan IEEE Transactions on Neural Networks and Learning Systems 35 (4), 5721-5734, 2024 | 91 | 2024 |
| Feature selection based on quality of information J Liu, Y Lin, M Lin, S Wu, J Zhang Neurocomputing 225, 11-22, 2017 | 85 | 2017 |
| Multi-label feature selection with streaming labels Y Lin, Q Hu, J Zhang, X Wu Information Sciences 372, 256-275, 2016 | 81 | 2016 |
| Mutual information based multi-label feature selection via constrained convex optimization Z Sun, J Zhang, L Dai, C Li, C Zhou, J Xin, S Li Neurocomputing 329, 447-456, 2019 | 80 | 2019 |
| Joint imbalanced classification and feature selection for hospital readmissions G Du, J Zhang, Z Luo, F Ma, L Ma, S Li Knowledge-Based Systems 200, 106020, 2020 | 76 | 2020 |
| Graph-based class-imbalance learning with label enhancement G Du, J Zhang, M Jiang, J Long, Y Lin, S Li, KC Tan IEEE Transactions on Neural Networks and Learning Systems 34 (9), 6081-6095, 2023 | 46 | 2023 |
| Group-preserving label-specific feature selection for multi-label learning J Zhang, H Wu, M Jiang, J Liu, S Li, Y Tang, J Long Expert Systems with Applications 213, 118861, 2023 | 45 | 2023 |
| Feature selection for multi-label learning with streaming label J Liu, Y Li, W Weng, J Zhang, B Chen, S Wu Neurocomputing 387, 268-278, 2020 | 45 | 2020 |
| Computational drug repositioning using collaborative filtering via multi-source fusion J Zhang, C Li, Y Lin, Y Shao, S Li Expert Systems with Applications 84, 281-289, 2017 | 39 | 2017 |
| Towards a unified multi-source-based optimization framework for multi-label learning J Zhang, C Li, Z Sun, Z Luo, C Zhou, S Li Applied Soft Computing 76, 425-435, 2019 | 36 | 2019 |
| ASFS: A novel streaming feature selection for multi-label data based on neighborhood rough set J Liu, Y Lin, J Du, H Zhang, Z Chen, J Zhang Applied Intelligence 53 (2), 1707-1724, 2023 | 28 | 2023 |
| Learning from weakly labeled data based on manifold regularized sparse model J Zhang, S Li, M Jiang, KC Tan IEEE Transactions on Cybernetics 52 (5), 3841-3854, 2022 | 28 | 2022 |
| Learning from class-imbalance and heterogeneous data for 30-day hospital readmission G Du, J Zhang, S Li, C Li Neurocomputing 420, 27-35, 2021 | 26 | 2021 |
| Semi-supervised partial multi-label classification via consistency learning A Tan, J Liang, WZ Wu, J Zhang Pattern recognition 131, 108839, 2022 | 20 | 2022 |
| Towards graph-based class-imbalance learning for hospital readmission G Du, J Zhang, F Ma, M Zhao, Y Lin, S Li Expert Systems with Applications 176, 114791, 2021 | 20 | 2021 |
| Fuzzy rough discrimination and label weighting for multi-label feature selection A Tan, J Liang, WZ Wu, J Zhang, L Sun, C Chen Neurocomputing 465, 128-140, 2021 | 19 | 2021 |
| Multi‐label feature selection with application to TCM state identification L Dai, J Zhang, C Li, C Zhou, S Li Concurrency and Computation: Practice and Experience 31 (23), e4634, 2019 | 18 | 2019 |
Shaozi Li厦门大学智能科学与技术系教授在 xmu.edu.cn 的电子邮件经过验证
