[1] 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)

[2] 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)

[3] 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)

[4] 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)

[5] 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)

[6] 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)

[7] 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)

[8] 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)

[9] 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)

[10] 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)

[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, 2021, 10.1109/TKDE.2021.3114204.

[2] 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.

[3] Wei Wei, Da Wang, Jiye Liang. Accelerating ReliefF using information granulation, International Journal of Machine Learning and Cybernetics, 2021, https://doi.org/10.1007/s13042-021-01334-4.

[4] 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.

[5] Wei Wei, Jiye Liang, Xinyao Guo, Peng Song,Yijun Sun. Hierarchical division clustering framework for categorical data, Neurocomputing, 2019, 341:118-134.

[6] Wei Wei, Jiye Liang. Information fusion in rough set theory : An overview, Information Fusion, 2019, 48:107-118.

[7] Peng Song, Jiye Liang, Yuhua Qian, Wei Wei, Feng Wang. A cautious ranking methodology with its application for stock screening, Applied Soft Computing, 2018, 71:835-848.

[8] 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.

[9] Wei Wei, Junbiao Cui, Jiye Liang, Junhong Wang. Fuzzy rough approximations for set-valued data, Information Sciences, 2016, 360:181-201.

[10] Wei Wei, Junhong Wang, Jiye Liang, Xin Mi, Chuangyin Dang. Compacted decision tables based attribute reduction, Knowledge-Based Systems, 2015, 86:261-277.

[11] 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.

[12] 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.

[13] Jiye Liang, Junrong Mi, Wei Wei, Feng Wang. An accelerator for attribute reduction based on perspective of objects and attributes, Knowledge-Based Systems, 2013, 44:90-100.

[14] Yuhua Qian, Jiye Liang, Peng Song, Chuangyin Dang, Wei Wei. Evaluation of the decision performance of the decision rule set from an ordered decision table. Knowledge-Based Systems, 2012, 36: 39-50.

[15] Yuhua Qian, Jiye Liang, Wei Wei. Consistency-preserving attribute reduction in fuzzy rough set framework. International Journal of Maching Learning and Cybernetics, 2012, 2012:45-53.

[16] Wei Wei, Jiye Liang, Yuhua Qian. A comparative study of rough sets for hybrid data. Information Sciences, 2012, 190:1-16.

[17] 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.

[18] Jiye Liang, Wei Wei, Yuhua Qian. An incremental approach to computation of a core based on conditional entropy. Systems Engineering-Theory & Practice, 2008, 4: 81-89.