| An effective collaborative filtering algorithm based on user preference clustering J Zhang, Y Lin, M Lin, J Liu Applied Intelligence 45 (2), 230-240, 2016 | 77 | 2016 |
| Feature selection based on quality of information J Liu, Y Lin, M Lin, S Wu, J Zhang Neurocomputing 225, 11-22, 2017 | 56 | 2017 |
| Multi-label feature selection with streaming labels Y Lin, Q Hu, J Zhang, X Wu Information Sciences 372, 256-275, 2016 | 49 | 2016 |
| 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 | 43 | 2019 |
| 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 | 43 | 2018 |
| 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 | 25 | 2019 |
| 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 | 22 | 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 | 18 | 2019 |
| 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 | 9 | 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 | 6 | 2020 |
| 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 | 5 | 2020 |
| A fast feature selection method based on mutual information in multi-label learning Z Sun, J Zhang, Z Luo, D Cao, S Li CCF Conference on Computer Supported Cooperative Work and Social Computing …, 2018 | 5 | 2018 |
| 基于目标用户近邻修正的协同过滤算法 张佳， 林耀进， 林梦雷， 刘景华 模式识别与人工智能 28 (9), 802-810, 2015 | 5 | 2015 |
| Granular matrix-based knowledge reductions of formal fuzzy contexts Y Lin, J Li, A Tan, J Zhang International Journal of Machine Learning and Cybernetics 11 (3), 643-656, 2020 | 4 | 2020 |
| Multi-label Feature Selection via Global Relevance and Redundancy Optimization J Zhang, Y Lin, M Jiang, S Li, Y Tang, KC Tan IJCAI, 2512-2518, 2020 | 3 | 2020 |
| Joint multilabel classification and feature selection based on deep canonical correlation analysis L Dai, G Du, J Zhang, C Li, R Wei, S Li Concurrency and Computation: Practice and Experience 32 (22), e5864, 2020 | 1 | 2020 |
| Learning From Weakly Labeled Data Based on Manifold Regularized Sparse Model J Zhang, S Li, M Jiang, KC Tan IEEE Transactions on Cybernetics, 2020 | 1 | 2020 |
| 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 | | 2021 |
| Identification of Autistic Risk Candidate Genes and Toxic Chemicals via Multilabel Learning ZA Huang, J Zhang, Z Zhu, EQ Wu, KC Tan IEEE Transactions on Neural Networks and Learning Systems, 2020 | | 2020 |
| A Microcosmic Syndrome Differentiation Model for Metabolic Syndrome with Multilabel Learning S Xia, J Zhang, G Du, S Li, CT Vong, Z Yang, J Xin, L Zhu, B Gao, C Li Evidence-Based Complementary and Alternative Medicine 2020, 2020 | | 2020 |
