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
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,澳门新甫京娱乐娱城平台学术委员会主任,澳门新甫京娱乐娱城平台计算智能与中文信息处理教育部重点实验室主任,曾任澳门新甫京娱乐娱城平台副校长、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年本科毕业于澳门新甫京娱乐娱城平台,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。主要从事人工智能、机器学习、大数据分析挖掘等方面的教学科研工作。
近年来先后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。先后在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3项、山西省教学成果特等奖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—2023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、CCF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] Jianqing Liang, Min Chen, Jiye Liang*. Graph external attention enhanced transformer[C]. International Conference on Machine Learning, 2024
[2] Wei Wei, Da Wang, Lin Li, Jiye Liang*. Re-attentive experience replay in off-policy reinforcement learning[J]. Machine Learning, 113(5): 2327-2349 (2024)
[3] 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
[4] 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
[5] Liang Bai, Minxue Qi, Jiye Liang*. Spectral clustering with robust self-learning constraints[J]. Artificial Intelligence, 2023, 320: 103924
[6] 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
[7] 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
[8] 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
[9] 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
[10] Junbiao Cui, Jianqing Liang, Qin Yue, Jiye Liang*. A general representation learning framework with generalization performance guarantees[C]. ICML2023
[11] Wei Wei, Lijun Zhang, Lin Li, Huizhong Song, Jiye Liang*. Set-membership belief state-based reinforcement learning for POMDPs[C]. ICML2023
[12] Ming Li*, Sho Sonoda*, Feilong Cao, Yu Guang Wang, Jiye Liang. How powerful are shallow neural networks with bandlimited random weights?[C]. ICML2023
[13] 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
[14] 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
[15] 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
[16] 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
[17] 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
[18] 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
[19] 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
[20] 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
[21] 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)
[22] 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)
[23] 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)
[24] 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)
[25] Junbiao Cui, Jiye Liang*. Fuzzy learning machine[C]. NeurIPS2022
[26] Liang Bai, Jiye Liang*, Yunxiao Zhao. Self-constrained spectral clustering[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, 45(4): 5126-5138
[27] 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
[28] 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
[29] Kaixuan Yao, Jiye Liang*, Jianqing Liang, Ming Li, Feilong Cao. Multi-view graph convolutional networks with attention mechanism[J]. Artificial Intelligence, 2022, 307: 103708
[30] Wei Wei, Yujia Zhang, Jiye Liang*, Lin Li, Yuze Li. Controlling underestimation bias in reinforcement learning via quasi-median operation[C]. AAAI2022
[31] Yunxia Wang, Fuyuan Cao*, Kui Yu, Jiye Liang. Efficient causal structure learning from multiple interventional datasets with unknown targets[C]. AAAI2022
[32] Qingqiang Chen, Fuyuan Cao*, Ying Xing, Jiye Liang. Instance selection: A bayesian decision theory perspective[C]. AAAI2022
[33] Qin Yue, Jiye Liang*, Junbiao Cui, Liang Bai. Dual bidirectional graph convolutional networks for zero-shot node classification[C]. KDD2022
[34] 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
[35] 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, 35(4): 4465-4477 (2024)
[36] 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)
[37] 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
[38] 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)
[39] 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
[40] 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
[41] 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
[42] 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)
[43] Liang Bai, Jiye Liang*. Sparse subspace clustering with entropy-norm[C]. ICML2020
[44] Liang Bai, Jiye Liang*. A three-level optimization model for nonlinearly separable clustering[C]. AAAI2020
[45] Jing Liu, Fuyuan Cao, Xiaozhi Gao, Liqin Yu, Jiye Liang*. A cluster-weighted kernel K-Means method for multi-view clustering[C]. AAAI2020
[46] Yinfeng Meng, Jiye Liang*. Linear regularized functional logistic model[J]. Journal of Computer Research and Development, 2020, 57(8): 1617-1626. (In Chinese)
[47] 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
[48] 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)
[49] 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)
[50] 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
[51] 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
[52] 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)
[53] 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
[54] 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
[55] 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)
[56] 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)
[57] 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)
[58] 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)
[59] 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
[60] 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
[61] 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
[62] 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)
[63] 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)
[64] 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)
[65] 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)
[66] 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
[67] 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
[68] 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)
[69] 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)
[70] 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
[71] 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
[72] 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)
[73] 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)
[74] 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
[75] 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)
[76] 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
[77] 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)
[78] 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
[79] 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
[80] 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
[81] 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)
[82] 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)
[83] 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)
[84] 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)
[85] 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)
[86] 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