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  • 梁吉业

    最终学位:博士

    导师类型:博士生导师

  • 电子邮箱:ljy@sxu.edu.cn

    联系电话:0351-7010566

  • 研究方向:数据挖掘与机器学习、大数据分析技术、人工智能

  • 个人简介
  • 学术论文
  • 科研项目

梁吉业,博士,教授,博士生导师,中国计算机学会(CCF)会士,中国人工智能学会(CAAI)会士,山西大学学术委员会主任委员,山西大学计算智能与中文信息处理教育部重点实验室主任,曾任山西大学副校长、太原师范学院院长。现任教育部科技委人工智能与区块链/科技伦理专门委员会委员,教育部高等学校计算机类专业教指委委员,中国计算机学会理事,中国人工智能学会理事,中国计算机学会人工智能与模式识别专委会主任,山西省计算机学会理事长,享受国务院政府特殊津贴专家。任国际学术期刊《Engineering Applications of Artificial Intelligence》、《International Journal of Computer Science and Knowledge Engineering》、国内学术期刊《计算机研究与发展》与《模式识别与人工智能》等期刊编委;是山西省高等学校优秀创新团队带头人、山西省首批科技创新重点团队带头人;入选山西省“三晋英才”支持计划高端领军人才、山西省高等学校中青年拔尖创新人才、山西省新世纪学术技术带头人333人才工程;获得山西省五一劳动奖章、第五届山西省青年科学家奖、山西省模范教师、山西省优秀研究生导师等多项荣誉称号。

1983年本科毕业于山西大学,获学士学位;1990年、2001年研究生毕业于西安交通大学,分别获硕士、博士学位;2002年至2004年在中国科学院计算技术研究所从事博士后研究工作。先后赴美国、德国、瑞士、瑞典、加拿大、日本、香港等国家和地区的大学进行学术访问和合作研究。主要从事大数据分析挖掘、机器学习、人工智能等方面的教学科研工作。

近年来主持科技部“科技创新2030—新一代人工智能”重大项目1项、国家自然科学基金/联合基金重点项目4项、国家863计划项目2项、国家自然科学基金面上项目6项等。在AI、JMLR、IEEE TPAMI、IEEE TKDE、ML、NeurIPS、ICML、AAAI等国际国内重要学术期刊和会议发表论文300余篇,其中SCI收录200余篇。作为第一完成人获山西省自然科学一等奖2项、第五届中国国际发明展览会金奖1项;作为第二完成人获山西省科技进步一等奖2项。2014—2022年连续入选爱思唯尔中国高被引学者榜单。指导的博士生获得全国百篇优秀博士学位论文提名奖、中国计算机学会优秀博士学位论文奖、中国人工智能学会优秀博士学位论文奖、中国中文信息学会优秀博士学位论文奖。

[1] Jiye Liang*, Zijin Du, Jianqing Liang, Kaixuan Yao, Feilong Cao. Long and short-range dependency graph structure learning framework on point cloud[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023, 45(12): 14975-14989.

[2] Qingqiang Chen, Fuyuan Cao*, Ying Xing, Jiye Liang*. Evaluating classification model against bayes error rate[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023, 45(8): 9639-9653.

[3] Liang Bai, Minxue Qi, Jiye Liang*. Spectral clustering with robust self-learning constraints[J]. Artificial Intelligence, 2023, 320: 103924.

[4] Wei Wei, Qin Yue, Kai Feng, Junbiao Cui, Jiye Liang*. Unsupervised dimensionality reduction based on fusing multiple clustering results[J]. IEEE Transactions on Knowledge and Data Engineering, 2023, 35(3): 3211-3223.

[5] Fuyuan Cao, Qingqiang Chen*, Ying Xing, Jiye Liang. Efficient classification by removing bayesian confusing samples[J]. IEEE Transactions on Knowledge and Data Engineering, 2023, DOI: 10.1109/TKDE.2023.3303425.

[6] Haijun Zhang, Xian Yang, Liang Bai*, Jiye Liang. Enhancing drug recommendations via heterogeneous graph representation learning in EHR networks[J]. IEEE Transactions on Knowledge and Data Engineering, 2023, DOI: 10.1109/TKDE.2023.3329025.

[7] Yu Xie, Zhiguo Qin, Maoguo Gong, Bin Yu, Jiye Liang*. Random deep graph matching[J]. IEEE Transactions on Knowledge and Data Engineering, 2023, 35(10): 10411-10422.

[8] Wei Wei, Qin Yue, Kai Feng, Junbiao Cui, Jiye Liang*. Unsupervised dimensionality reduction based on fusing multiple clustering results[J]. IEEE Transactions on Knowledge and Data Engineering, 2023, 35(3): 3211-3223.

[9] Junbiao Cui, Jianqing Liang, Qin Yue, Jiye Liang*. A general representation learning framework with generalization performance guarantees[C]. ICML2023.

[10] Wei Wei, Lijun Zhang, Lin Li, Huizhong Song, Jiye Liang*. Set-membership belief state-based reinforcement learning for POMDPs[C]. ICML2023.

[11] Ming Li*, Sho Sonoda*, Feilong Cao, Yu Guang Wang, Jiye Liang. How powerful are shallow neural networks with bandlimited random weights?[C]. ICML2023.

[12] Yujie Wang, Hu Zhang*, Jiye Liang*, Ru Li. Dynamic heterogeneous-graph reasoning with language models and knowledge representation learning for commonsense question answering[C]. ACL2023.

[13] Yunxia Wang, Fuyuan Cao*, Kui Yu, Jiye Liang. Local causal discovery in multiple manipulated datasets[J]. IEEE Transactions on Neural Networks and Learning Systems, 2023, 34(10): 7235-7247.

[14] Yuling Li, Kui Yu*, Yuhong Zhang, Jiye Liang, Xindong Wu. Adaptive prototype interaction network for few-shot knowledge graph completion[J]. IEEE Transactions on Neural Networks and Learning Systems, 2023, DOI: 10.1109/TNNLS.2023.3283545.

[15] Liang Bai, Jiye Liang*. K-relations-based consensus clustering with entropy-norm regularizers[J]. IEEE Transactions on Neural Networks and Learning Systems, 2023, DOI: 10.1109/TNNLS.2023.3307158.

[16] Yecheng Guo, Liang Bai*, Xian Yang, Jiye Liang. Improving image contrastive clustering through self-Learning pairwise constraints[J]. IEEE Transactions on Neural Networks and Learning Systems, 2023, DOI: 10.1109/TNNLS.2023.3329494.

[17] Jieting Wang, Feijiang Li, Jue Li, Chenping Hou, Yuhua Qian*, Jiye Liang. RSS-bagging: improving generalization through the fisher information of training data[J]. IEEE Transactions on Neural Networks and Learning Systems, 2023, DOI: 10.1109/TNNLS.2023.3270559.

[18] Jianli Huang, Xianjie Guo, Kui Yu*, Fuyuan Cao, Jiye Liang. Towards privacy-aware causal structure learning in federated setting[J]. IEEE Transactions on Big Data, 2023, 9(6): 1525-1535.

[19] Shujing Yang, Fuyuan Cao*, Kui Yu, Jiye Liang. Learning causal chain graph structure via alternate learning and double pruning[J]. IEEE Transactions on Big Data, 2023, DOI: 10.1109/TBDATA.2023.3346712.

[20] Xingwang Zhao, Shujun Wang, Xiaolin Liu, Jiye Liang*. Joint spectral embedding multi-view clustering algorithm based on bipartite graphs[J]. Journal of Software, 2023, DOI: 10.13328/j.cnki.jos.006995. (in Chinese)

[21] Xingwang Zhao, Yaopu Zhang, Jiye Liang*. Two-stage ensemble-based community discovery algorithm in multilayer networks[J]. Journal of Computer Research and Development, 2023, 60(12): 2832-2843. (in Chinese)

[22] Tao Yan, Yuhua Qian*, Feijiang Li, Hongren Yan, Jieting Wang, Jiye Liang, et al. Intelligent microscopic 3D shape reconstruction method based on 3D time-frequency transformation[J]. Scientia Sinica Informationis, 2023, 53: 282-308. (in Chinese)

[23] Jieting Wang, Feijiang Li, Jue Li, Yuhua Qian*, Jiye Liang. Gini index and decision tree method with mitigating random consistency[J]. Scientia Sinica Informationis, DOI: 10.1360/SSI-2022-0337. (in Chinese)

[24] Junbiao Cui, Jiye Liang*. Fuzzy learning machine[C]. NeurIPS2022.

[25] Liang Bai, Jiye Liang*, Yunxiao Zhao. Self-constrained spectral clustering[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, 45(4): 5126-5138.

[26] Xinyan Liang, Yuhua Qian*, Qian Guo, Honghong Cheng, Jiye Liang, AF: An association-based fusion method for multi-modal classification[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, 44(12): 9236-9254.

[27] Jieting Wang, Yuhua Qian*, Feijiang Li, Jiye Liang, Qingfu Zhang. Generalization performance of pure accuracy and its application in selective ensemble learning[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, 45(2): 1798-1816.

[28] Kaixuan Yao, Jiye Liang*, Jianqing Liang, Ming Li, Feilong Cao. Multi-view graph convolutional networks with attention mechanism[J]. Artificial Intelligence, 2022, 307: 103708.

[29] Wei Wei, Yujia Zhang, Jiye Liang*, Lin Li, Yuze Li. Controlling underestimation bias in reinforcement learning via quasi-median operation[C]. AAAI2022.

[30] Yunxia Wang, Fuyuan Cao*, Kui Yu, Jiye Liang. Efficient causal structure learning from multiple interventional datasets with unknown targets[C]. AAAI2022.

[31] Qingqiang Chen, Fuyuan Cao*, Ying Xing, Jiye Liang. Instance selection: A bayesian decision theory perspective[C]. AAAI2022.

[32] Qin Yue, Jiye Liang*, Junbiao Cui, Liang Bai. Dual bidirectional graph convolutional networks for zero-shot node classification[C]. KDD2022.

[33] Yu Xie , Shengze Lv, Yuhua Qian*, Chao Wen, Jiye Liang. Active and semi-supervised graph neural networks for graph classification[J]. IEEE Transactions on Big Data, 2022, 8: 920-932.

[34] Jie Wang, Jianqing Liang, Jiye Liang*, Kaixuan Yao. GUIDE: Training deep graph neural networks via guided dropout over edges [J]. IEEE Transactions on Neural Networks and Learning Systems, 2022, DOI: 10.1109/TNNLS.2022.3172879.

[35] Xiaolin Liu, Liang Bai, Xingwang Zhao, Jiye Liang*. Incomplete multi-view clustering algorithm based on multi-order neighborhood diffusion and fusion[J]. Journal of Software, 2022, 33(4): 1354−1372. (in Chinese)

[36] Jiye Liang*, Xiaolin Liu, Liang Bai, Fuyuan Cao, Dianhui Wang. Incomplete multi-view clustering via local and global co-regularization[J]. SCIENCE CHINA Information Sciences, 2022, 65(5): 152105.

[37] Keqi Wang, Yuhua Qian*, Jiye Liang, Chang Liu, Qin Huang, et al. Local-global coupling relationship based low-light image enhancement[J]. Scientia Sinica Informationis, 2022, 52(3): 443-460. (in Chinese)

[38] Liang Bai, Jiye Liang*, Fuyuan Cao. Semi-supervised clustering with constraints of different types from multiple information sources[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021, 43(9): 3247-3258.

[39] Gaoxia Jiang, Wenjian Wang*, Yuhua Qian, Jiye Liang. A unified sample selection framework for output noise filtering: An error-bound perspective[J]. Journal of Machine Learning Research, 2021, 22(18): 1−66.

[40] Jiye Liang*, Junbiao Cui, Jie Wang, Wei Wei. Graph-based semi-supervised learning via improving the quality of the graph dynamically[J]. Machine Learning, 2021, 110: 1345-1388.

[41] Chenjiao Feng, Peng Song, Zhiqiang Wang, Jiye Liang*. A method on long tail recommendation based on three-factor probabilistic graphical model[J]. Journal of Computer Research and Development, 2021, 58(9):1975-1986. (in Chinese)

[42] Liang Bai, Jiye Liang*. Sparse subspace clustering with entropy-norm[C]. ICML2020.

[43] Liang Bai, Jiye Liang*. A three-level optimization model for nonlinearly separable clustering[C]. AAAI2020.

[44] Jing Liu, Fuyuan Cao, Xiaozhi Gao, Liqin Yu, Jiye Liang*. A cluster-weighted kernel K-Means method for multi-view clustering[C]. AAAI2020.

[45] Yinfeng Meng, Jiye Liang*. Linear regularized functional logistic model[J]. Journal of Computer Research and Development, 2020, 57(8): 1617-1626. (In Chinese)

[46] Jiye Liang*, Yunsheng Song, Deyu Li, Zhiqiang Wang, Chuangyin Dang. An accelerator for the logistic regression algorithm based on sampling on-demand[J]. SCIENCE CHINA Information Sciences, 2020, 63(6): 169102.

[47] Honghong Cheng, Yuhua Qian*, Zhiguo Hu, Jiye Liang. Association mining method based on neighborhood[J]. Scientia Sinica Informationis, 2020, 50(6): 824-844. (In Chinese)

[48] Feijiang Li, Yuhua Qian*, Jieting Wang, Jiye Liang, Wenjian Wang. Clustering method based on sample's stability[J]. Scientia Sinica Informationis, 2020, 50(8): 1239-1254. (In Chinese)

[49] Liang Bai, Jiye Liang*, Hangyuan Du, Yike Guo. An information-theoretical framework for cluster ensemble[J]. IEEE Transactions on Knowledge and Data Engineering, 2019, 31(8): 1464-1477.

[50] Anhui Tan*, Weizhi Wu, Yuhua Qian, Jiye Liang, Jinkun Chen, et al. Intuitionistic fuzzy rough set-based granular structures and attribute subset selection[J]. IEEE Transactions on Fuzzy Systems, 2019, 27(3): 527-539.

[51] Zhiqiang Wang, Jiye Liang*, Ru Li. Probability matrix factorization for link prediction based on information fusion[J]. Journal of Computer Research and Development, 2019, 56(2): 306-318. (In Chinese)

[52] Liang Bai, Jiye Liang*, Yike Guo. An ensemble clusterer of multiple fuzzy k-means clusterings to recognize arbitrarily shaped clusters[J]. IEEE Transactions on Fuzzy Systems, 2018, 26(6): 3524-3533.

[53] Fuyuan Cao, Joshua Zhexue Huang*, Jiye Liang, Xingwang Zhao, Yinfeng Meng. An Algorithm for Clustering Categorical Data with Set-valued Features[J]. IEEE Transactions on Neural Networks and Learning Systems, 2018, 29(10): 4593-4606.

[54] Jiye Liang*, Jie Qiao, Fuyuan Cao, Xiaolin Liu. A distributed representation model for short text analysis[J]. Journal of Computer Research and Development, 2018, 55(8): 1631-1640. (in Chinese)

[55] Qinghua Hu*, Yu Wang, Yucan Zhou, Hong Zhao, Yuhua Qian, Jiye Liang. Review on hierarchical learning methods for large-scale classification task[J]. Scientia Sinica Informationis, 2018, 48(5): 487-500. (In Chinese)

[56] Kaihan Zhang, Jiye Liang*, Xingwang Zhao, Zhiqiang Wang. A collaborative filtering recommendation algorithm based on information of community experts[J]. Journal of Computer Research and Development, 2018, 55(5): 968-976. (In Chinese)

[57] Yali Lü, Jiajie Wu, Jiye Liang, Yuhua Qian. Random search learning algorithm of BN based on super-structure[J]. Journal of Computer Research and Development, 2017, 54(11): 2558-2566. (In Chinese)

[58] Zhiqiang Wang, Jiye Liang*, Ru Li, Yuhua Qian. An approach to cold-start link prediction: establishing connections between non-topological and topological information[J]. IEEE Transactions on Knowledge and Data Engineering, 2016, 28(11): 2857-2870.

[59] Liang Bai, Xueqi Cheng, Jiye Liang*, Huawei Shen. An optimization model for clustering categorical data streams with drifting concepts[J]. IEEE Transactions on Knowledge and Data Engineering, 2016, 28(11): 2871-2883.

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

[61] Xingwang Zhao, Jiye Liang*. An attribute weighted clustering algorithm for mixed data based on information entropy[J]. Journal of Computer Research and Development, 2016, 53(5): 1018-1028. (in Chinese)

[62] Qianyu Shi, Jiye Liang, Xingwang Zhao*. A clustering ensemble algorithm for incomplete mixed data[J]. Journal of Computer Research and Development, 2016, 53(9): 1979-1989. (in Chinese)

[63] Jiye Liang*, Chenjiao Feng, Peng Song. A survey on correlation analysis of big data[J]. Chinese Journal of Computers,2016, 39(1): 1-18. (in Chinese)

[64] Zhiqiang Wang, Ru Li*, Jiye Liang, Xuhua Zhang, Juan Wu, et al. Research on question answering for reading comprehension based on Chinese discourse frame semantic parsing[J]. Chinese Journal of Computers, 2016, 39(4): 795-807. (in Chinese)

[65] Yuhua Qian, Yebin Li, Jiye Liang, Guoping Lin, Chuangyin Dang. Fuzzy granular structure distance[J]. IEEE Transactions on Fuzzy Systems, 2015, 23(6): 2245-2259.

[66] Yuhua Qian, Hang Xu, Jiye Liang, Bing Liu, Jieting Wang. Fusing monotonic decision trees[J]. IEEE Transactions on Knowledge and Data Engineering, 2015, 27(10): 2717-2728.

[67] Jiye Liang*, Yuhua Qian, Deyu Li, Qinghua Hu. Theory and method of granular computing for big data mining[J]. Scientia sinica informationis, 2015, 45(11): 1355-1369. (in Chinese)

[68] Jie Wang, Jiye Liang, Wenping Zheng*. A graph clustering method for detecting protein complexes[J]. Journal of Computer Research and Development, 2015, 52(8): 1784-1793. (in Chinese)

[69] Jiye Liang*, Feng Wang, Chuangyin Dang, Yuhua Qian. A group incremental approach to feature selection applying rough set technique[J]. IEEE Transactions on Knowledge and Data Engineering, 2014, 26(2):294-308.

[70] Liang Bai, Jiye Liang*, Chuangyin Dang, Fuyuan Cao. The impact of cluster representatives on the convergence of the K-Modes type clustering[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(6): 1509-1522.

[71] Xiaofang Gao, Jiye Liang*. Manifold learning algorithm DC-ISOMAP of data lying on the well-separated multi-manifold with same intrinsic dimension[J]. Journal of Computer Research and Development, 2013, 50(8): 1690-1699. (in Chinese)

[72] Ru Li, Zhiqiang Wang, Shuanghong Li, Jiye Liang, Collin Baker. Chinese Sentence Similarity Computing Based on Frame Semantic Parsing[J]. Journal of Computer Research and Development, 2013, 50(8): 1728-1736. (in Chinese).

[73] Jiye Liang*, Liang Bai, Chuangyin Dang, Fuyuan Cao. The k-means-type algorithms versus imbalanced data distributions[J]. IEEE Transactions on Fuzzy Systems, 2012, 20(4): 728-745.

[74] Xuefei Bai, Wenjian Wang, Jiye Liang*. An active contour model based on region saliency for image segmentation[J]. Journal of Computer Research and Development, 2012, 49(12): 2686-2695. (in Chinese)

[75] Yuhua Qian, Jiye Liang*, Weizhi Wu, Chuangyin Dang. Information granularity in fuzzy binary GrC model[J]. IEEE Transactions on Fuzzy Systems, 2011, 19(2): 253-264.

[76] Yuhua Qian, Jiye Liang*, Feng Wang. A positive approximation based accelerated algorithm to feature selection from incomplete decision tables[J]. Chinese Journal of Computers, 2011, 34(3): 435-442. (in Chinese)

[77] Yuhua Qian, Jiye Liang*, Witold Pedrycz, Chuangyin Dang. Positive approximation: an accelerator for attribute reduction in rough set theory[J]. Artificial Intelligence, 2010, 174: 597-618.

[78] Fuyuan Cao, Jiye Liang*, Liang Bai, Xingwang Zhao, Chuangyin Dang. A framework for clustering categorical time-evolving data[J]. IEEE Transactions on Fuzzy Systems, 2010, 18(5):872-882.

[79] Yuhua Qian, Jiye Liang*, Chuangyin Dang. Incomplete multigranulation rough set[J]. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 2010, 40(2): 420-431.

[80] Jiye Liang*, Liang Bai, Fuyuan Cao. K-modes clustering algorithm based on a new distance measure[J]. Journal of Computer Research and Development, 2010, 47(10): 1749-1755. (in Chinese)

[81] Jianmei Zhang, Shiqun Tao, Jiye Liang, Feng Cao. Reasoning about Structural Integrity Constraints for XML[J]. Chinese Journal of Computers, 2010, 33(12): 2281-2290. (in Chinese)

[82] Kaishe Qu*, Yanhui Zhai, Jiye Liang, Deyu Li. Representation and extension of rough set theory Based on formal concept analysis[J]. Journal of Software, 2007, 18(9): 2174-2182. (in Chinese)

[83] Jiye Liang*, Junhong Wang. An algorithm for extracting rule-generating sets based on concept lattice[J]. Journal of Computer Research and Development, 2004, 41(8): 1339-1344. (in Chinese)

[84] Feilong Cao, Zongben Xu, Jiye Liang. Approximation of polynomial functions by neural network: construction of network and algorithm of approximation[J]. Chinese Journal of Computers, 2003, 26(8): 906-912. (in Chinese)

[85] Jiye Liang, Zongben Xu, Yuexiang Li. Inclusion degree and measures of rough set data analysis[J]. Chinese Journal of Computers, 2001, 24(5): 544-547. (in Chinese)

1. 国家自然科学基金委员会,联合基金重点项目,U21A20473,网络大数据分析挖掘的理论与方法,2022-01至2025-12,主持

2. 国家自然科学基金委员会,面上项目,62376141,知识引导的开放集学习方法研究,2024.01至2027.12,主持

3. 国家科技部,科技创新2030—“新一代人工智能”重大项目,2020AAA0106100,认知计算基础理论与方法研究,2020-11至2024-10,主持

4. 国家自然科学基金委员会,面上项目,61876103,基于多粒度的半监督学习方法,2019-01至2022-12,主持

5. 国家自然科学基金委员会,重点项目/总装联合基金项目,61432011/U1435212,面向大数据的粒计算理论与方法,2015-01至2019-12,主持

6. 国家自然科学基金委员会,重点项目,71031006,高维复杂数据分析理论及其在投资决策中的应用,2011-01至2014-12,主持

7. 国家科技部,973计划前期研究专项,2011CB11805,基于认知机理的高维复杂数据建模理论与方法,2011-01至2012-12,主持

8. 国家自然科学基金委员会,面上项目,70971080,面向复杂数据的粗糙集多属性/多准则决策分析研究,2010-01至2012-12,主持

9. 国家自然科学基金委员会,面上项目,60773133,复杂信息系统的粒度结构与知识获取研究,2008-01至2010-12,28万元,已结题,主持

10. 国家科技部,863计划项目,2007AA01Z165,面向高维复杂数据的粒度计算理论与算法研究,2007-10至2009-12,主持

11. 国家自然科学基金委员会,面上项目,70471003,基于软计算技术的不确定性决策方法研究,2005-01至2007-12,主持

12. 国家科技部,863计划项目,2004AA115460,专家系统及计算机软硬件系统评价技术研究,2004-10至2005-12,主持

13. 国家自然科学基金委员会,面上项目,60275019,粗糙集理论中的不确定性、模糊性与知识获取,2003-01至2005-12,主持