李飞江 - 计算机与信息技术学院（大数据学院）

李飞江，博士，副教授，硕士生导师，2020年7月毕业于山西大学计算机应用技术专业。主要研究方向包括机器学习、无监督学习、集成学习的基础理论与方法。近年来，在《Artificial Intelligence》、《IEEE Transactions on Pattern Analysis and Machine Intelligence》、《Machine Learning》、《ACM Transactions on Knowledge Discovery from Data》、《IEEE Transactions on Neural Network and Learning Systems》、AAAI、IJCAI、《中国科学》等国内外重要学术期刊与会议发表论文三十余篇。主持国家自然科学基金面上项目、青年项目、重点专项课题、山西省重大专项课题，参与国家级、省部级项目十余项。曾获山西省自然科学奖一等奖，山西省优秀博士学位论文，ACM太原分会优秀博士论文奖、教育部宝钢优秀学生奖、山西大学第七届青年五四奖章。

更多信息见：https://feijiangli.github.io/

[1] Feijiang Li, Yuhua Qian, Jieting Wang, Chuangyin Dang, Liping Jing, Clustering ensemble based on sample’s stability, Artificial Intelligence, 2019, 273: 37-55

[2] Jieting Wang, Yuhua Qian, Feijiang Li, Jiye Liang, Qingfu Zhang, Generalization Performance of Pure Accuracy and Its Application in Selective Ensemble Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023, 45(2): 1798-1816

[3] Feijiang Li, Yuhua Qian, Jieting Wang, Chuangyin Dang, Bing Liu. Cluster’s quality evaluation and selective clustering ensemble. ACM Transactions on Knowledge Discovery from Data, 2018, 12(5): 60

[4] Yuhua Qian, Feijiang Li, Jiye Liang, Bing Liu, Chuangyin Dang. Space structure and clustering of categorical data. IEEE Transactions on Neural Networks and Learning Systems, 2016, 27(10): 2047-2059

[5] Feijiang Li, Jieting Wang, Yuhua Qian, Guoqing Liu, Keqi Wang, Fuzzy Ensemble Clustering Based on Self Co-Association and Prototype Propagation, IEEE Transactions on Fuzzy Systems, 2023, 31(10): 3610-3623

[6] Feijiang Li, Yuhua Qian, Jieting Wang, Jiye Liang. Multigranulation information fusion: A Dempster-Shafer evidence theory-based clustering ensemble method. Information Sciences, 2017, 378: 389-409

[7] Jieting Wang, Yuhua Qian, Feijiang Li. Learning with Mitigating Random Consistency from the Accuracy Measure, Machine Learning, 2020, 109: 2247-2281

[8] Jieting Wang, Feijiang Li, Jue Li, Chenping Hou, Yuhua Qian, Jiye Liang. RSS-Bagging: Improving Generalization through the Fisher Information of Training Data. IEEE Transactions on Neural Networks and Learning Systems, 2025, 36(2): 1974-1988

[9] Jieting Wang, Yuhua Qian, Feijiang Li, Jiye Liang, Weiping Ding. Fusing fuzzy monotonic decision trees. IEEE Transactions on Fuzzy Systems, 2020, 28(5): 887-900

[10] Yuhua Qian, Hu Zhang, Feijiang Li, Qinghua Hu, Jiye Liang. Set-Based Granular Computing: a Lattice Model. International Journal of Approximate Reasoning, 2014, 55(3): 834 852

[11] Feijiang Li, Yuhua Qian, Jieting Wang, Furong Peng, Jiye Liang, Clustering mixed type data: a space structure-based approach, International Journal of Machine Learning and Cybernetics, 2022, 13: 2799-2812

[12] Tao Li, Yuhua Qian, Feijiang Li, Xinyan Liang, Zhihui Zhan, Feature Subspace Learning-based Binary Differential Evolution Algorithm for Unsupervised Feature Selection, IEEE Transactions on Big Data; 2024, DOI: 10.1109/TBDATA.2024.3378090

[13] Jing Pan, Yuhua Qian, Feijiang Li, Qian Guo, Image deep clustering based on local-topology embedding, Pattern Recognition Letters, 2021, 151: 88-94

[14] Feijiang Li, Yuhua Qian, Jieting Wang, GoT: a growing tree model for clustering ensemble. The Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021, 35(9): 8349-8356

[15] Furong Peng, Jiachen Luo, Xuan Lu, Sheng Wang, Feijiang Li, Cross-Domain Contrastive Learning for Time Series Clustering, Proceedings of the AAAI Conference on Artificial Intelligence, 2024, 38(8): 8921-8929

[16] Zhanwen Cheng, Feijiang Li, Jieting Wang, Yuhua Qian, Deep Embedding Clustering Driven by Sample Stability, Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, 2024: 3854-3862

[17] Hongren Yan, Yuhua Qian, Furong Peng, Jiachen Luo, zheqing Zhu, Feijiang Li, Deep Neural Collapse To Multiple Centers For Imbalanced Data, Proceedings of the Advances in Neural Information Processing Systems 37, 2024, 37: 65583-65617

[18] Feijiang Li, Jieting Wang, Liuya zhang, Yuhua Qian, Shuai Jin, Tao Yan, Liang Du, k-HyperEdge Medoids for Clustering Ensemble, Proceedings of the AAAI Conference on Artificial Intelligence, 2025

[19] Quanjiang Li, Tingjin Luo, Mingdie Jiang, Zhangqi Jiang, Chenping Hou, Feijiang Li, Semi-Supervised Multi-View Multi-Label Learning with View-Specific Transformer and Enhanced Pseudo-Label, Proceedings of the AAAI Conference on Artificial Intelligence, 2025

[20] Liang Du, Henghui Jiang, Xiaodong Li, Yiqing Guo, Yan Chen, Feijiang Li, Peng Zhou, Yuhua Qian, Sharper Error Bounds in Late Fusion Multi-view Clustering with Eigenvalue Proportion Optimization, Proceedings of the AAAI Conference on Artificial Intelligence, 2025

[21] Feijiang Li, Yuhua Qian, Jieting Wang, Prototype Propagation Clustering Based on Large Margin, International Conference on Data Mining Workshops, 2019: 639-645

[22] 李飞江, 钱宇华, 王婕婷, 梁吉业, 王文剑. 基于样本稳定性的聚类方法. 中国科学: 信息科学, 2020, 50(8): 1239-1254

[23] 王婕婷, 李飞江, 李珏, 钱宇华, 梁吉业. 缓解随机一致性的基尼指数与决策树方法. 中国科学: 信息科学, 2024, 54(1): 159-190

[24] 闫涛, 钱宇华, 李飞江, 闫泓任, 王婕婷, 梁吉业, 郑珂银, 吴鹏, 陈路, 胡治国, 乔志伟, 张江峰, 翟小鹏. 三维时频变换视角的智能微观三维形貌重建方法. 中国科学: 信息科学, 2023, 53(2): 282-308

[25] 王婕婷, 钱宇华, 李飞江, 刘郭庆. 消除随机一致性的支持向量机分类方法. 计算机研究与发展, 2020, 57(8): 1581-1593

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[2] 国家自然科学基金青年项目, 多粒度视角下的无监督集成学习有效性理论与方法, 2022-01-01至2024-12-31, 主持

[3] 国家自然科学基金专项项目课题, 知识驱动的高可靠数据集构建方法研究, 2025-01-01至2026-12-31, 主持

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[5] 山西省应用基础研究计划项目(青年基金), 20210302124271, 聚类集成的视觉可解释性理论与方法研究, 2022-01至2024-12, 主持

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[10] 中央引导地方科技发展资金项目, 基于无人系统技术的空间三维信息感知平台, 2023年1月至2026年12月, 参与