国家自然科学基金青年基金, 2012.01-2014.12, 主持人

江苏省自然科学基金面上项目, 2011.07-2014.12, 主持人

江苏省高校自然科学基金, 2011.08-2013.12, 主持人

中国博士后科学基金, 2012.05-2013.05, 主持人

江苏省青蓝工程优秀青年骨干教师, 2012.12-2015.01, 主持人

江苏省青蓝工程中青年学术带头人, 2014.05-2017.08, 主持人

国家自然科学基金面上项目, 2016.01-2019.12, 主持人

国家自然科学基金面上项目, 2015.01-2018.12, 排名第二

国家自然科学基金面上项目，2021.01-2024.12，主持人

[1] 杨习贝, 王长宝, 胡广朋, 董海燕. 具有防止小孩被困 ATM 机防护舱的舱门电子系统及实现方法, 专利号: ZL201610270966.6, 授权公告日: 2018 年 4 月 17 日.

[2] 杨习贝, 王长宝, 胡广朋, 董海燕. 基于多传感防止被困 ATM 机防护舱舱门电子系统及实现方法, 专利号: ZL201610242307.1, 授权公告日: 2018 年 4 月 13 日.

[3] 杨习贝, 王长宝, 张明, 胡广朋, 董海燕.探人灵敏度受声控的 ATM 机防护舱舱门电子系统及实现方法, 专利号: ZL201610244763.X, 授权公告日: 2017 年 12 月 15 日.

[4] 杨习贝, 李京政, 吴陈, 王长宝, 胡广朋, 董海燕. 基于多传感防止被困 ATM 机防护舱舱门电子系统及实现方法, 专利号: ZL201610273231.9, 授权公告日: 2017 年 12 月 12 日.

[1] Xibei Yang, Jingyu Yang. Incomplete information system and rough set theory: Models and attribute reductions, Science Press & Springer, 2012. (Science Press ISBN: 978-7-03-032476-4, Springer ISBN: 978-3-642-25934-0)
[2] Xibei Yang*, Jingyu Yang, Chen Wu, Dongjun Yu. Dominance-based rough set approach and knowledge reductions in incomplete ordered information system, Information Sciences, Vol. 178, No. 4, 2008, pp. 1219-1234. 
[3] Xbei Yang*, Jun Xie, Xiaoning Song, Jingyu Yang. Credible rules in incomplete decision system based on descriptors, Knowledge-Based Systems, Vol. 22, No. 1, 2009, pp. 8-17. 
[4] Xibei Yang*, Tsau Young Lin, Jingyu Yang, Yan Li, Dongjun Yu. Combination of interval-valued fuzzy set and soft set, Computers & Mathematics with Applications, Vol. 58, No. 3, 2009, pp. 521-527. 
[5] Xibei Yang*, Dongjun Yu, Jingyu Yang, Lihua Wei. Dominance-based rough set approach to incomplete interval-valued information system, Data & Knowledge Engineering, Vol. 68, No. 11, 2009, pp. 1331–1347.
[6] Xibei Yang*, Dongjun Yu, Jingyu Yang, Xiaoning Song. Difference relation-based rough set and negative rules in incomplete information system, International Journal of Uncertainty, Fuzziness and Knowledge-based Systems, Vol. 17, No. 5, 2009, pp. 649-665. 
[7] Xibei Yang*, Ming Zhang, Huili Dou, Jingyu Yang. Neighborhood systems-based rough sets in incomplete information system, Knowledge-Based Systems, Vol. 24, No. 6, 2011, pp. 858-867. 
[8] Xibei Yang*, Ming Zhang. Dominance-based fuzzy rough approach to an interval-valued decision system, Frontier of Computer Science in China, Vol. 5, No. 2, 2011, pp. 195-204. 
[9] Xibei Yang*, Zehua Chen, Huili Dou, Ming Zhang, Jingyu Yang, Neighborhood system based rough set: Models and attribute reductions, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, Vol. 20, No. 3, 2012, pp. 399-419.
[10] Xibei Yang*, Yuhua Qian, Jingyu Yang, Hierarchical structures on multigranulation spaces, Journal of Computer Science and Technology, Vol. 27, No. 6, 2012, pp. 1169-1183. 

[11] Xibei Yang*, Yanqin Zhang, Jingyu Yang, Local and global measurements of MGRS rules, International Journal of Computational Intelligence Systems, Vol. 5, No. 6, 2012, pp. 1010-1024.

[12] Xibei Yang*, Xiaoning Song, Zehua Chen, Jingyu Yang, On multigranulation rough sets in incomplete information system, International Journal of Machine Learning & Cybernetics, Vol. 3, No. 3, 2012, pp. 223-232.

[13] Lijuan Wang*, Xibei Yang, Jingyu Yang, Chen Wu, Relationships among generalized rough sets in six coverings and pure reflexive neighborhood system, Information Sciences, Vol. 207, 2012, pp. 35-49. 

[14] Xibei Yang*, Yunsong Qi, Xiaoning Song, Jingyu Yang, Test cost sensitive multigranulation rough set: Model and minimal cost selection, Information Sciences, Vol. 250, 2013, pp. 184-199. 
[15] Xibei Yang*, Xiaoning Song, Yanhong She, Jingyu Yang, Hierarchy on multigranulation structures: A knowledge distance approach, International Journal of General Systems, Vol. 42, No. 7, 2013, pp. 754-773. 

[16] Xibei Yang*, Yuhua Qian, Jingyu Yang, On characterizing hierarchies of granulation structures via distances, Fundamenta Informaticae, Vol. 123, No. 3, 2013, pp. 365-380.

[17] Yunsong Qi*, Xibei Yang, Interval-valued analysis for discriminative gene selection and tissue sample classification using microarray data, Genomics, Vol. 101, No. 1, 2013, pp. 38-48. 

[18] Xibei Yang*, Yong Qi, Hualong Yu, Xiaoning Song, Jingyu Yang, Updating multigranulation rough approximations with increasing of granular structures, Knowledge-Based Systems, Vol. 64, 2014, pp. 59-69. 
[19] Xibei Yang*, Xiaoning Song, Yunsong Qi, Jingyu Yang, Constructive and axiomatic approaches to hesitant fuzzy rough set, Soft Computing, Vol. 18, No. 6, 2014, pp. 1067-1077. 
[20] Jingjing Song, Xibei Yang*, Xiaoning Song, Hualong Yu, Jingyu Yang, Hierarchies on fuzzy information granulations: A knowledge distance based lattice approach, Journal of Intelligent & Fuzzy Systems, Vol. 27, No. 3, 2014, pp. 1107-1117. (研究生一作)

[21] Hengrong Ju, Xibei Yang*, Xiaoning Song, Yunsong Qi, Dynamic updating multigranulation fuzzy rough set: approximations and reducts, International Journal of Machine Learning & Cybernetics, Vol. 5, No. 6, 2014, pp. 981-990. (研究生一作)

[22] Zaiyue Zhang, Xibei Yang*, Tolerance-based multigranulation rough sets in incomplete systems, Frontiers of Computer Science, Vol. 8, No. 5, 2014, pp. 753-762. 

[23] Xibei Yang*, Yong Qi, Dong-Jun Yu, Hualong Yu, Jingyu Yang, alpha–Dominance relation and rough sets in interval-valued information systems, Information Sciences, Vol. 294, 2015, pp. 334-347.

[24] Suping Xu, Xibei Yang*, Hualong Yu, Dong-Jun Yu, Jingyu Yang, Eric C. C. Tsang, Multi-label learning with label-specific feature reduction, Knowledge-Based Systems, Vol. 104, 2016, pp. 52-61. (研究生一作)

[25] Hengrong Ju, Xibei Yang*, Hualong Yu, Tongjun Li, Dong-Jun Yu, Jingyu Yang, Cost-sensitive rough set approach, Information Sciences, Vol. 355-356, 2016, pp. 282-298. (研究生一作)

[26] Huili Dou, Xibei Yang*, Xiaoning Song, Hualong Yu, Wei-Zhi Wu, Jingyu Yang, Decision-theoretic rough set: A multicost strategy, Knowledge -Based Systems, Vol. 91, 2016, pp. 71-83.

[27] Xibei Yang*, Suping Xu, Huili Dou, Xiaoning Song, Hualong Yu, Jingyu Yang, Multigranulation rough set: A multiset based strategy, International Journal of Computational Intelligence Systems, Vol. 10, No. 1, 2017, pp. 277-292.

[28] Xibei Yang*, Yiyu Yao, Ensemble selector for attribute reduction, Applied Soft Computing, Vol. 70, 2018, pp. 1-11.

[29] Shaochen Liang, Xibei Yang*, Xiangjian Chen, Jingzheng Li, Stable attribute reduction for neighborhood rough set, Filomat, Vol. 32, No. 5, 2018, pp. 1809-1815. (研究生一作)

[30] Jingzheng Li, Xiangjian Chen, Pingxin Wang, Xibei Yang*, Local view based cost-sensitive attribute reduction, Filomat, Vol. 32, No. 5, 2018, pp. 1817-1822. (研究生一作)

[31] Xibei Yang*, Shaochen Liang, Hualong Yu, Shang Gao, Yuhua Qian, Pseudo-label neighborhood rough set: Measures and attribute reductions, International Journal of Approximate Reasoning, Vol. 105, 2019, pp. 112-129.

[32] Keyu Liu, Xibei Yang*, Hamido Fujita, Dun Liu, Xin Yang, Yuhua Qian, An efficient selector for multi-granularity attribute reduction, Information Sciences, Vol. 505, 2019, pp. 457-472. (研究生一作)

[33] Keyu Liu, Xibei Yang*, Hualong Yu, Jusheng Mi, Pingxin Wang, Xiangjian Chen, Rough set based semi-supervised feature selection via ensemble selector, Knowledge-Based Systems, Vol. 165, 2019, pp. 282-296. (研究生一作)

[34] Zehua Jiang, Xibei Yang*, Hualong Yu, Dun Liu, Pingxin Wang, Yuhua Qian, Accelerator for multi-granularity attribute reduction, Knowledge-Based Systems, Vol. 177, 2019, pp. 145-158. (研究生一作)

[35] Jingzheng Li, Xibei Yang*, Xiaoning Song, Jinhai Li, Pingxin Wang, Dong-Jun Yu, Neighborhood attribute reduction: A multi-criterion approach, International Journal of Machine Learning & Cybernetics, Vol. 10, No. 4, 2019, pp. 731-742. (研究生一作)

[36] Zehua Jiang, Keyu Liu, Xibei Yang*, Hualong Yu, Hamido Fujita, Yuhua Qian, Accelerator for supervised neighborhood based attribute reduction, International Journal of Approximate Reasoning, Vol. 119, 2020, pp. 122-150. (研究生一作)

[37] Keyu Liu, Xibei Yang*, Hualong Yu, Hamido Fujita, Xiangjian Chen, Dun Liu, Supervised information granulation strategy for attribute reduction, International Journal of Machine Learning & Cybernetics, Vol. 11, No. 9, 2020, pp. 2149-2163. (研究生一作)

[38] Yan Chen, Keyu Liu, Jingjing Song, Hamido Fujita*, Xibei Yang, Yuhua Qian, Attribute group for attribute reduction, Information Sciences, Vol. 535, 2020, pp. 64-80. (研究生一作)

[39] Xiansheng Rao, Xibei Yang*, Xin Yang, Xiangjian Chen, Dun Liu, Yuhua Qian, Quickly calculating reduct: An attribute relationship based approach, Knowledge-Based Systems, Vol. 200, 2020, Article 106014.(研究生一作)

[40] Xiansheng Rao, Keyu Liu, Jingjing Song, Xibei Yang*, Yuhua Qian, Gaussian kernel fuzzy rough based attribute reduction: An acceleration approach, Journal of Intelligent & Fuzzy Systems, Vol. 39, No. 1, 2020, pp. 679-695. (研究生一作)

[41]  Zehua Jiang, Huili Dou*, Jingjing Song, Pingxin Wang, Xibei Yang, Yuhua Qian,  Data-guided multi-granularity selector for attribute reduction, Applied Intelligence, Vol. 51, No. 2, 2021, pp. 876-888. (研究生一作)

[42]  Zehua Jiang, Keyu Liu, Jingjing Song, Xibei Yang*, Jinhai Li, Yuhua Qian, Accelerator for crosswise computing reduct, Applied Soft Computing, Vol. 98, 2021, Article 106740. (研究生一作)

[43]  Yan Chen, Pingxin Wang, Xibei Yang*, Jusheng Mi, Dun Liu, Granular ball guided selector for attribute reduction, Knowledge-Based Systems, Vol. 229, 2021, Article 107326. (研究生一作)

[44]  Jing Ba, Keyu Liu, Hengrong Ju, Suping Xu, Taihua Xu, Xibei Yang*, Triple-G: A new MGRS and attribute reduction, International Journal of Machine Learning & Cybernetics, Vol. 13, No. 2, 2022, pp. 337-356. (研究生一作)

[45]  Zhen Chen, Keyu Liu, Xibei Yang*, Hamido Fujita, Random sampling accelerator for attribute reduction, International Journal of Approximate Reasoning,  Vol. 140, 2022, pp. 75-91. (研究生一作)

[46] Yan Chen, Xibei Yang*,  Jinhai Li, Pingxin Wang, Yuhua Qian, Fusing attribute reduction accelerators, Information Sciences, Vol. 587, 2022, pp. 354-370. (研究生一作)

[47] Zhice Gong, Yuxin Liu, Taihua Xu*, Pingxin Wang, Xibei Yang, Unsupervised attribute reduction: Improving effectiveness and efficiency, International Journal of Machine Learning & Cybernetics, Vol. 13, No. 11, 2022, pp. 3645-3662. (本科生一作)

[48] Yining Chen, Pingxin Wang, Xibei Yang*, Hualong Yu, BEE: Towards a robust attribute reduction, International Journal of Machine Learning & Cybernetics, Vol. 13, No. 12, 2022, pp. 3927-3962.(研究生一作)

[49] Yuxin Liu, Zhice Gong, Keyu Liu, Suping Xu, Hengrong Ju, Xibei Yang*, A Q-learning approach to attribute reduction, Applied Intelligence, Vol. 53, No. 4, 2023, pp. 3750-3765. (本科生一作)

[50] Jiaqi Sun, Pingxin Wang, Hualong Yu, Xibei Yang*, A constraint score guided meta-heuristic searching to attribute reduction,  Journal of Intelligent & Fuzzy Systems, Vol. 44, No. 3, 2023, pp. 4779-4800. (研究生一作)

[51] Jiadong Zhang, Keyu Liu*, Xibei Yang, Hengrong Ju, Suping Xu, Multi-label learning with Relief-based label-specific feature selection, Applied Intelligence, Vol. 53, No. 15, 2023, pp. 18517-18530. (研究生一作)

[52] Jing Ba, Pingxin Wang, Xibei Yang*, Hualong Yu, Dongjun Yu, GLEE: A granularity filter for feature selection, Engineering Applications of Artificial Intelligence, Vol. 122, 2023, Article 106080. (研究生一作)

[53] Keyu Liu, Tianrui Li*, Xibei Yang, Hongmei Chen, Jie Wang, Zhixuan Deng, SemiFREE: Semi-supervised feature selection with fuzzy relevance and redundancy, IEEE Transactions on Fuzzy Systems, Vol. 31, No. 10, 2023, pp. 3384-3396.

[54] Zhenyu Yin, Xibei Yang*, Pingxin Wang, Hualong Yu, Yuhua Qian, Ensemble selector mixed with pareto optimality to feature reduction, Applied Soft Computing, Vol. 148, 2023, Article 110877.  (研究生一作)

[55] Jing Ba, Keyu Liu, Xibei Yang*, Yuhua Qian, Gift: granularity over specific-class for feature selection, Artificial Intelligence Review, Vol. 56, No. 10, 2023, pp. 12201-12232. (研究生一作)

[56] Xiaoxiao Wang, Xibei Yang*, Pingxin Wang, Hualong Yu, Taihua Xu, SSGCN: A sampling sequential guided graph convolutional network, International Journal of Machine Learning & Cybernetics, Vol. 15, No, 5, 2024, pp. 2023-2038. (研究生一作)

[57] Qiguo Sun*, Xueying Wei, Xibei Yang, GraphSAGE with deep reinforcement learning for financial portfolio optimization, Expert Systems with Applications, Vol. 238, Part C, 2024, Article 122027. 

[58] Qihang Guo, Xibei Yang*, Fengjun Zhang, Taihua Xu, Perturbation-augmented graph convolutional networks: A graph contrastive learning architecture for effective node classification tasks, Engineering Applications of Artificial Intelligence, Vol. 129, 2024, Article 107616. (研究生一作)

[59] Hui Cong, Qiguo Sun*, Xibei Yang, Keyu Liu, Yuhua Qian, Enhancing graph convolutional networks with progressive granular-ball sampling fusion: A novel approach to efficient and accurate GCN training, Information Sciences, Vol. 676, 2024, Article 120831. (研究生一作)

[60] Damo Qian, Keyu Liu*, Shiming Zhang, Xibei Yang, Semi-supervised feature selection by minimum neighborhood redundancy and maximum neighborhood relevancy, Applied Intelligence, Vol. 54, No. 17-18, 2024, pp. 7750-7764. (本科生一作)

[61] Jie Ben, Qiguo Sun*, Keyu Liu,  Xibei Yang, Fengjun Zhang, Multi-head multi-order graph attention networks, Applied Intelligence, Vol. 54, No. 17-18, 2024, pp. 8092-8107. (本科生一作)

[62] Qing Teng, Xibei Yang, Qiguo Sun, Pingxin Wang, Xun Wang, Taihua Xu*, Sequential attention layer-wise fusion network for multi-view classification, International Journal of Machine Learning & Cybernetics, Vol. 15, No. 12, 2025, pp. 5549-5561. (研究生一作)

[63] Qihang Guo, Keyu Liu, Taihua Xu, Pingxin Wang, Xibei Yang*,  Fuzzy feature factorization machine: Bridging feature interaction, selection, and construction, Expert Systems with Applications, Vol. 255, Part C, 2024, Article 124600. (研究生一作)

[64] Yuge Wang, Xibei Yang, Qiguo Sun*, Yuhua Qian, Qihang Guo, Purity skeleton dynamic hypergraph neural network, Neurocomputing, Vol. 610, 2024, Article 128539. (研究生一作)

[65] Qihang Guo, Xibei Yang*, Ming Li, Yuhua Qian, Collaborative graph neural networks for augmented graphs: A local-to-global perspective, Pattern Recognition, Vol. 158, 2025, Article 111020.  (研究生一作)

[66] Qihang Guo, Xibei Yang*,  Wenrui Guan, Kai Ma, Yuhua Qian, Robust graph mutual-assistance convolutional networks for semi-supervised node classification tasks, Information Sciences, Vol. 694, 2025, Article 121708. (研究生一作)

[67] Qiguo Sun*, Xibei Yang, Meiyu Zhong, Forecasting copper price with multi-view graph transformer and fractional Brownian motion-based data augmentation, Natural Resources Research, Vol. 35, No. 1, 2025, pp. 253-269. 

[68] Guoquan Zhu, Keyu Liu*, Xibei Yang, Qihang Guo, UMGCN: Updating multi-graph for graph convolutional networks, Computers and Electrical Engineering, Vol. 123, Part A, 2025, Article 109957. (研究生一作)

[69] Qihang Guo, Xibei Yang*, Weiping Ding, Yuhua Qian, Cross-graph interaction networks, IEEE Transactions on Knowledge and Data Engineering, Vol. 37, No. 4, 2025, pp. 2341-2355. (研究生一作)

[70] Hui Cong, Xibei Yang*, Keyu Liu, Qihang Guo, Feature-topology cascade perturbation for graph neural network, Engineering Applications of Artificial Intelligence, Vol. 152, 2025, Article 110657. (研究生一作)

[71] Qing Teng, Keyu Liu*, Xibei Yang, Ming Li, Inconsistency-aware graph convolutional networks for multi-view classification,  IEEE Transactions on Emerging Topics in Computational Intelligence, Accepted. (研究生一作)

[72] Yifan Zheng, Xibei Yang*, Qiguo Sun, Keyu Liu, Qihang Guo, A multi-hop Shapley-based framework for graph convolutional network node classification explanation, Applied Soft Computing, Vol. 184, Part B, 2025, Article 113615. (研究生一作)

王平心(1980-)，江苏科技大学副教授，硕士生导师，中国人工智能学会粒计算与知识发现专业委员会委员，主要研究方向为粒计算与三支决策。发表学术论文30余篇，主持国家自然科学基金项目1项。

http://mypage.just.edu.cn/sl/wpx/list.htm

徐泰华(1989-)，西南交通大学博士，现为江苏科技大学讲师，硕士生导师。发表学术论文10余篇，主持国家自然科学基金项目1项。

宋晶晶(1990-)，江苏科技大学硕士(导师: 杨习贝)，澳门科技大学博士，现为江苏科技大学副教授，硕士生导师，中国人工智能学会粒计算与知识发现专业委员会委员，主要研究方向为粒计算与知识发现。发表学术论文10余篇，主持国家自然科学基金项目1项。

鞠恒荣(1989-)，江苏科技大学硕士(导师: 杨习贝)，南京大学博士，现为南通大学副教授，硕士生导师，主要研究方向为粒计算与机器学习。江苏省优秀专业硕士学位论文获得者，发表学术论文20余篇，被引50余次，主持国家自然科学基金项目1项，江苏省高校自然科学基金项目1项，入选江苏省双创博士计划。

徐苏平(1991-)，江苏科技大学硕士(导师: 杨习贝)，主要研究方向为粒计算与多标记学习。江苏省优秀学术学位硕士学位论文获得者，在IJCAI(CCF A)等会议、期刊上发表论文10余篇，现为南京大学博士研究生。

李京政(1993-)，江苏科技大学硕士(导师: 杨习贝)，主要研究方向为粒计算与机器学习。江苏科技大学优秀硕士学位论文获得者，发表SCI期刊论文2篇，现为北京航空航天大学博士研究生。

刘克宇(1994-)，江苏科技大学硕士生(导师: 杨习贝)，主要研究方向为粒计算与机器学习。江苏省优秀学术学位硕士学位论文获得者，发表SCI期刊论文5篇（4篇一区），现为西南交通大学博士研究生。

姜泽华(1997-)，江苏科技大学硕士生(导师: 杨习贝)，主要研究方向为粒计算与机器学习。江苏科技大学优秀硕士学位论文获得者，发表SCI期刊论文4篇（2篇一区，1篇三区，1篇CCF B），现就职于海康威视软件开发岗。

陈妍(1996-)，江苏科技大学硕士生(导师: 杨习贝)，主要研究方向为粒计算与机器学习。发表SCI期刊论文4篇（3篇一区）。

饶先胜(1995-)，江苏科技大学硕士(导师: 杨习贝)，主要研究方向为粒计算与机器学习。江苏科技大学优秀硕士学位论文获得者，发表SCI期刊论文2篇（1篇一区），现就职于中国工商银行六安分行信息科技岗。

巴婧(1997-)，江苏科技大学硕士生(导师: 杨习贝)，主要研究方向为粒计算与机器学习。录用SCI期刊论文三区1篇。

陈振(1994-)，江苏科技大学硕士生(导师: 杨习贝)，主要研究方向为粒计算与机器学习。发表CCF B类期刊论文1篇。

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