最终学位：博士
导师类型：博士生导师
电子邮箱：weiwei@sxu.edu.cn
联系电话：0351-7018949
研究方向：数据挖掘、机器学习、无人系统
魏巍，博士、教授、博士生导师，山西大学计算机与信息技术学院（大数据学院）副院长。长期从事数据挖掘、机器学习、无人系统等方面的研究；先后赴香港城市大学、美国纽约州立大学布法罗分校做访问学者；担任中国人工智能学会（CAAI）知识工程与分布智能专委会副秘书长、粒计算与知识发现专业委员会常委，中国计算机学会人工智能与模式识别专业委员会执行委员、大数据专家委员会执行委员、青年计算机科技论坛（YOCSEF）太原分论坛2022-2023年度主席。
近年来，主持和参与国家重点研发计划项目、国家自然科学基金重点项目、国家自然科学基金面上项目、山西省自然科学基金项目10余项。重点围绕表示学习、强化学习等领域的基础科学问题开展系统研究，先后在《IEEE TKDE》、《Machine Learning》、《Pattern Recognition》、ICML、AAAI等重要学术期刊会议发表论文40余篇，获国家发明专利3项。
[1] Wei Wei, Qin Yue, Kai Feng, Junbiao Cui, Jiye Liang*. Unsupervised dimensionality reduction based on fusing multiple clustering results. IEEE Transactions on Knowledge and Data Engineering, 2023, 35(3): 3211-3223.
[2] Wei Wei, Lijun Zhang, Lin Li, Huizhong Song, Jiye Liang*. Set-membership belief state-based reinforcement learning for POMDPs. Proceedings of the International Conference on Machine Learning, 2023, 36856-36867.
[3] Wei Wei, Yujia Zhang, Jiye Liang*, Lin Li, Yuze Li. Controlling underestimation bias in reinforcement learning via quasi-median operation. Proceedings of the AAAI Conference on Artificial Intelligence, 2022, 36(8): 8621-8628.
[4] Wei Wei, Da Wang, Lin Li, Jiye Liang*. Re-attentive Experience Replay in Off-policy Reinforcement Learning. Machine Learning, 2024, 10.1007/s10994-023-06505-8.
[5] Jing Yan, Wei Wei*, Xinyao Guo, Chuangyin Dang, Jiye Liang. A bi-level metric learning framework via self-paced learning weighting. Pattern Recognition, 2023, 139: 109446.
[6] Lin Li, Yuze Li, Wei Wei*, Yujia Zhang, Jiye Liang. Multi-actor mechanism for actor-critic reinforcement learning. Information Sciences, 2023, 647: 119494.
[7] Xinyao Guo, Wei Wei*, Jianqing Liang, Chuangyin Dang, Jiye Liang. Metric learning via perturbing hard-to-classify instances. Pattern Recognition, 2022, 132: 108928.
[8] Jiye Liang*, Junbiao Cui, Jie Wang, Wei Wei. Graph-based semi-supervised learning via improving the quality of the graph dynamically. Machine Learning, 2021, 110: 1345–1388.
[9] Wei Wei, Da Wang, Jiye Liang*. Accelerating ReliefF using information granulation. International Journal of Machine Learning and Cybernetics, 2022, 13(1): 29-38.
[10] Lin Li, Ting Li, Wei Wei*, Xinyao Guo, Jiye Liang. Hierarchical metric learning with intra-level and inter-level regularization. International Journal of Machine Learning and Cybernetics, 2022, 13(12): 4033-4042.
[11] Wei Wei, Peng Song*, Jiye Liang, Xiaoying Wu. Accelerating incremental attribute reduction algorithm by compacting a decision table. International Journal of Machine Learning and Cybernetics, 2019, 10(9): 2355-2373.
[12] Wei Wei, Jiye Liang, Xinyao Guo, Peng Song*,Yijun Sun. Hierarchical division clustering framework for categorical data. Neurocomputing, 2019, 341: 118-134.
[13] Wei Wei, Jiye Liang*. Information fusion in rough set theory : An overview. Information Fusion, 2019, 48: 107-118.
[14] Wei Wei, Xiaoying Wu, Jiye Liang*, Junbiao Cui, Yijun Sun. Discernibility matrix based incremental attribute reduction for dynamic data. Knowledge-Based Systems, 2018, 140: 142-157.
[15] Wei Wei, Junbiao Cui, Jiye Liang*, Junhong Wang. Fuzzy rough approximations for set-valued data. Information Sciences, 2016, 360: 181-201.
[16] Wei Wei, Junhong Wang, Jiye Liang*, Xin Mi, Chuangyin Dang. Compacted decision tables based attribute reduction. Knowledge-Based Systems, 2015, 86: 261-277.
[17] Wei Wei, Jiye Liang*, Junhong Wang, Yuhua Qian. Decision-relative discernibility matrixes in the sense of entropies. International Journal of General Systems, 2013, 42(7): 721-738.
[18] Wei Wei, Jiye Liang*, Yuhua Qian, Chuangyin Dang. Can fuzzy entropies be effective measures for evaluating the roughness of a rough set. Information Sciences, 2013, 232: 143-166.
[19] Wei Wei, Jiye Liang*, Yuhua Qian. A comparative study of rough sets for hybrid data. Information Sciences, 2012, 190: 1-16.
[20] Wei Wei, Jiye Liang*, Yuhua Qian, Feng Wang, Chuangyin Dang. Comparative study of decision performance of decision tables induced by attribute reductions. International Journal of General Systems, 2010, 39(8): 813-838.
[1] 魏巍, 冯宇轩, 李琳, 梁吉业, 司瑞华, 王达, 一种基于深度强化学习的无人机运动规划方法及系统, 2022-12-27, 中国, ZL202211679084.7
[2] 魏巍, 王达, 李琳, 梁吉业, 一种基于深度强化学习的机械臂控制方法及系统, 2021-12-29, 中国, ZL202111645521.9
[3] 李琳, 李涛, 魏巍, 崔军彪, 一种融合多个源域的跨域行人重识别方法及系统, 2020-12-2, 中国, ZL202011399651.4
[1] 国家自然科学基金面上项目(No. 62276160): 基于粒计算的多视图度量学习模型与算法研究, 2023.01－2026.12 (主持)
[2] 国家自然科学基金面上项目(No. 61772323): 面向高维数据的粒计算理论与方法, 2018.01－2021.12 (主持)
[3] 国家重点研发计划项目(No. 2019YFE0118200): 基于自组网无人机群的污染气体激光监测平台, 2020.12－2023.11 (第三参与)
[4] 国家自然科学基金面上项目(No.61976184): 度量学习的优化技术与高效算法研究, 2020.01－2023.12 (第二参与)
[5] 国家自然科学基金青年项目(No. 61303008): 混合数据多粒度粗糙计算模型与算法研究, 2014.01－2016.12 (主持)
[6] 国家自然科学基金重点项目(No. 61432011 ): 面向大数据的粒计算理论与方法, 2015.01－2019.12 (参与)
[7] 国家自然科学基金青年项目(No.61202018): 多概念格集成与知识获取方法研究, 2013.01－2015.12 (参与)
[8] 山西省自然科学基金面上项目(No. 202203021211291): 基于粒计算的多视图深度度量学习方法研究, 2023.01-2025.12 (主持)
[9] 山西省自然科学基金面上项目(No. 202203021211294): 面向深度强化学习的值函数估计方法研究, 2023.01-2025.12 (参与)
[10] 山西省自然科学基金青年项目(No.2013021018-1): 面向混合数据的粒度计算理论与方法研究, 2013.01－2015.12 (主持)
[11] 973计划前期研究专项(No.2011CB11805): 基于认知机理的高维复杂数据建模理论与方法, 2011.01－2012.12 (参与)
[12] 高等学校博士学科点专项科研基金: 基于粒计算的符号数据分析方法研究, 2011.01－2013.12 (参与)
[13] 山西省高校科技开发项目: 面向时间序列数据的概念格模型、算法及应用, 2010.05－2012.05 (参与)