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：

研究方向：数据挖掘、机器学习、智能计算、智能决策


个人简介
学术论文
科研项目
梁吉业，男，1962年1月生，博士，教授，博士生导师，中国计算机学会（CCF）会士，山西大学副校长（正校级），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，教育部计算机类专业教学指导委员会委员，中国计算机学会理事，中国人工智能学会理事，中国人工智能学会知识工程与分布智能专业委员会副主任委员，中国计算机学会大数据专家委员会委员，山西省计算机学会理事长，大数据挖掘与智能技术山西省协同创新中心主任，享受国务院政府特殊津贴专家。任国际学术期刊《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》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；首批入选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。
主要从事人工智能、数据挖掘与机器学习、智能决策等方面的教学科研工作。近年来，先后主持国家863计划项目2项、国家自然科学基金项目7项（其中重点基金项目2项），973计划前期研究专项1项、骨干项目2项，教育部博士点专项基金项目（博导类）2项，省部级项目20余项。在《Artificial Intelligence》、《IEEE Transactions on Pattern Analysis and Machine Intelligence》、《IEEE Transactions on Knowledge and Data Engineering》、《Pattern Recognition》、《Neural Networks》、《IEEE Transactions on Fuzzy Systems》、《IEEE Transactions on Systems, Man and Cybernetics-Part A》、《Omega: the International Journal of Management Science》、《Information Sciences》、《中国科学》、《计算机学报》、《中国管理科学》等国际国内重要学术刊物发表论文200余篇，其中SCI收录120余篇；由科学出版社出版著作2部；获国家发明专利8项。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。2014年—2018年，连续入选爱思唯尔中国高被引****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] Zhiqiang Wang, Jiye Liang, Ru Li. Exploiting user-to-user topic inclusion degree for link prediction in social-information networks, Expert Systems with Applications, 2018, 108:143-158.
[2] Liang Bai, Jiye Liang, Yike Guo. An ensemble clusterer of multiple fuzzy k-means clusterings to recognize arbitrarily shaped clusters, IEEE Transactions on Fuzzy Systems, 2018, DOI 10.1109/TFUZZ.2018.**..
[3] Zhiqiang Wang, Jiye Liang, Ru Li. A fusion probability matrix factorization framework for link prediction, Knowledge-Based Systems, 2018, 10.1016/j.knosys.2018.06.005.
[4] Yinfeng Meng, Jiye Liang, Fuyuan Cao, Yijun He. A new distance with derivative information for functional k-means clustering algorithm, Information Sciences, 2018, 10.1016/j.ins.2018.06.035.
[5] Liang Bai, Jiye Liang, Hangyuan Du, YikeGuo. A novel community detection algorithm based on simplification of complex networks, Knowledge-Based Systems, 2018, doi.org/10.1016/j.knosys.2017.12.007.
[6] 张凯涵, 梁吉业, 赵兴旺, 王智强. 一种基于社区专家信息的协同过滤推荐算法, 计算机研究与发展, 2018, 55(5):968-976.
[7] Xingwang Zhao, Fuyuan Cao, Jiye Liang. A sequential ensemble clusterings generation algorithm for mixed data, Applied Mathematics and Computation, 2018, 335:264–277.
[8] Fuyuan Cao, Joshua Zhexue Hang, Jiye Liang, Xingwang Zhao, Yinfeng Meng. An Algorithm for Clustering Categorical Data with Set-valued Features, IEEE Transactions on Neural Networks and Learning Systems, 2018, (DOI:10.1109/TNNLS.2017.**).
[9] 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.
[10] Xiaoqiang Guan, Jiye Liang, Yuhua Qian, Jifang Pang. A multi-view OVA model based on decision tree for multi-classification tasks, Knowledge-Based Systems, 2017, 138:208–219.
[11] Xingwang Zhao, Jiye Liang, Chuangyin Dang. Clustering ensemble selection for categorical data based on internal validity indices, Pattern Recognition, 2017, 69:150–168.
[12] Jifang Pang, Jiye Liang, Peng Song. An adaptive consensus method for multi-attribute group decision making under uncertain linguistic environment, Applied Soft Computing, 2017, 58:339-353.
[13] Yunsheng Song, Jiye Liang, Jing Lu, Xingwang Zhao. An efficient instance selection algorithm for k nearest neighbor regression, Neurocomputing, 2017, 251:26-34.
[14] Fuyuan Cao, Joshua Zhexue Huang, Jiye Liang. A fuzzy SV-k-modes algorithm for clustering categorical data with set-valued attributes, Applied Mathematics and Computation, 2017, 295:1–15.
[15] Yuhua Qian, Honghong Cheng, Jieting Wang, Jiye Liang, Witold Pedrycz, Chuangyin Dang. Grouping granular structures in human granulation intelligence, Information Sciences, 2017, 382-383:150–169.
[16] Liang Bai, Xueqi Chen, Jiye Liang, Huawei Shen, Yike Guo. Fast density clustering strategies based on the k-means algorithm, Pattern Recognition, 2017, 71:375–386.
[17] Liang Bai, Xueqi Cheng, Jiye Liang, Yike Guo. Fast graph clustering with a new description model for community detection, Information Sciences, 2017, 388-389:37–47.
[18] Jie Wang, Wenping Zheng, Yuhua Qian, Jiye Liang. A seed expansion graph clustering method for protein complexes detection in protein interaction networks, Molecules, 2017, 22:2179.
[19] Feijiang Li, Yuhua Qian, Jieting Wang, Jiye Liang. Multigranulation information fusion: A Dempster-Shafer evidence theory-based clustering ensemble method, Information Sciences, 2017, 378:389–409.
[20] Fuyuan Cao, Liqin Yu, Joshua Zhexue Huang, Jiye Liang. k-mw-modes: an algorithm for clustering categorical matrix-object data, Applied Soft Computing, 2017, 57:605-614.
[21] Yuhua Qian, Xinyan Liang, Guoping Lin, Qian Guo, Jiye Liang. Local multigranulation decision-theoretic rough sets, International Journal of Approximate Reasoning, 2017, 82:119-137.
[22] 梁吉业, 钱宇华, 李德玉, 胡清华. 面向大数据的粒计算理论与方法研究进展, 大数据, 2016, 4:13-23.
[23] 梁吉业, 冯晨娇, 宋鹏. 大数据相关分析综述, 计算机学报, 2016, 39(1):1-18.
[24] Yanli Sang, Jiye Liang, Yuhua Qian. Decision-theoreticroughsetsunderdynamicgranulation, Knowledge-Based Systems, 2016, 91:84-92.
[25] GuopingLin, Jiye Liang, Yuhua Qian, JinjinLi. A fuzzy multigranulation decision-theoretic approach to multi-source fuzzy information systems, Knowledge-Based Systems, 2016, 91:102-113.
[26] Yinfeng Meng, Jiye Liang, Yuhua Qian. Comparison study of orthonormal representations of functional data in classification, Knowledge-Based Systems, 2016, 97:224–236.
[27] Feng Wang, Jiye Liang. An efficient feature selection algorithm for hybrid data, Neurocomputing, 2016, 193:33–41.
[28] Zhiqiang Wang, Jiye Liang, Ru Li, Yuhua Qian. An approach to cold-start link prediction:establishing connections between non-topological and topological information, IEEE Transactions on Knowledge and Data Engineering, 2016, 28(11):2857- 2870.
[29] 史倩玉, 梁吉业, 赵兴旺. 一种不完备混合数据集成聚类算法, 计算机研究与发展, 2016, 53(9):1979-1989.
[30] 赵兴旺, 梁吉业. 一种基于信息熵的混合数据属性加权聚类算法, 计算机研究与发展, 2016, 53(5):1018-1028.
[31] 王宝丽, 梁吉业, 胡运红. 基于粒计算的犹豫模糊多准则决策方法, 模式识别与人工智能, 2016, 29(3):252-262.
[32] 郭兰杰, 梁吉业, 赵兴旺. 融合社交网络信息的协同过滤推荐算法, 模式识别与人工智能, 2016, 29(3):281-288.
[33] 梁晋, 梁吉业, 赵兴旺. 一种面向大规模社会网络的社区发现算法, 南京大学学报(自然科学版), 2016, 52(1):159-166.
[34] Liang Bai, Xueqi Cheng, Jiye Liang, Huawei Shen. An optimization model for clustering categorical data streams with drifting concepts, IEEE Transactions on Knowledge and Data Engineering, 2016, 28(11):2871-2883.
[35] Wei Wei, Junbiao Cui, Jiye Liang, Junhong Wang. Fuzzy rough approximations for set-valued data, Information Sciences, 2016, 360:181–201.
[36] Yuhua Qian, Feijiang Li, Jiye Liang, Bing Liu, Chuangyin Dang. Space structure and clustering of categorical data, IEEE Transactions on Neural Networks and Learning Systems, 2016, 27(10):2047-2059.
[37] 王智强, 李茹, 梁吉业, 张旭华, 武娟, 苏娜. 基于汉语篇章框架语义分析的阅读理解问答研究, 计算机学报, 2016, 39(4):795-807.
[38] 梁吉业, 钱宇华, 李德玉, 胡清华. 大数据挖掘的粒计算理论与方法, 中国科学(E辑:信息科学), 2015, 45(11):1355-1369.
[39] Liang Bai, Jiye Liang. Cluster validity functions for categorical data:a solution-space perspective, Data Mining and Knowledge Discovery, 2015, 29(6):1560-1597.
[40] Yuhua Qian, Jiye Liang, Chuangyin Dang. Fuzzy granular structure distance, IEEE Transactions on Fuzzy Systems, 2015, 23(6):2245-2259.
[41] Guoping Lin, Jiye Liang, Yuhua Qian. An information fusion approach by combining multigranulation rough sets and evidence theory, Information Sciences, 2015, 314:184–199.
[42] Baoli Wang, Jiye Liang, Yuhua Qian, Chuangyin Dang. A normalized numerical scaling method for the unbalanced multi-granular linguistic sets, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2015, 23(2):221-243.
[43] Guoping Lin, Jiye Liang, Yuhua Qian. Uncertainty measures for multigranulation approximation space, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2015, 23(3):443–457.
[44] Xiaofang Gao, Jiye Liang. An improved incremental nonlinear dimensionality reduction for isometric data embedding, Information Processing Letters, 2015, 115(4):492–501.
[45] 王杰, 梁吉业, 郑文萍. 一种面向蛋白质复合体检测的图聚类方法, 计算机研究与发展, 2015, 52(8):1784-1793.
[46] 林国平, 梁吉业, 李进金. 多源决策信息系统的决策规则性能评价, 模式识别与人工智能, 2015, 28(7):657-664.
[47] Yuhua Qian, Hang Xu, Jiye Liang, Bing Liu, Jieting Wang. Fusing monotonic decision trees, IEEE Transactions on Knowledge and Data Engineering, 2015, 27(10):2717-2728.
[48] Wei Wei, Junhong Wang, Jiye Liang, Xin Mi, Chuangyin Dang. Compacted decision tables based attribute reduction, Knowledge-Based Systems, 2015, 86:261-277.
[49] Yuhua Qian, Qi Wang, Honghong Cheng, Jiye Liang, Chuangyin Dang. Fuzzy-rough feature selection accelerator, Fuzzy Sets and Systems, 2015, 258:61–78.
[50] Jiye Liang, Feng Wang, Chuangyin Dang, Yuhua Qian. A group incremental approach to feature selection applying rough set technique, IEEE Transactions on Knowledge and Data Engineering, 2014, 26(2):294 - 308.
[51] Liang Bai, Jiye Liang. The k-modes type clustering plus between-cluster information for categorical data, Neurocomputing, 2014, 133:111–121.
[52] Baoli Wang, Jiye Liang, Yuhua Qian. Preorder information based attributes weights learning in multi-attribute decision making, Fundamenta Informaticae, 2014, 132:331-347.
[53] Guoping Lin, Jiye Liang, Yuhua Qian. Topological approach to multigranulation rough sets, International Journal of Machine Learning and Cybernetics, 2014, 5(2):233-243.
[54] Xingwang Zhao, Jiye Liang, Fuyuan Cao. A simple and effective outlier detection algorithm for categorical data, International Journal of Machine Learning and Cybernetics, 2014, 5(3):469–477.
[55] 郭一鹏, 梁吉业, 赵兴旺. 基于MapReduce的混合数据孤立点检测算法, 小型微型计算机系统, 2014, 35(9):1961-1966.
[56] Xin Liu, Yuhua Qian, Jiye Liang. A rule-extraction framework under multigranulation rough sets, International Journal of Machine Learning and Cybernetics, 2014, 5(2):319-326.
[57] Yuhua Qian, Shunyong Li, Jiye Liang, Zhongzhi Shi, Feng Wang. Pessimistic rough set based decisions: A multigranulation fusion strategy, Information Sciences, 2014, 264:196–210.
[58] Fuyuan Cao, Joshua Zhexue Huang, Jiye Liang. Trend analysis of categorical data streams with a concept change method, Information Sciences, 2014, 276:160–173.
[59] Yuhua Qian, Hu Zhang, Yanli Sang, Jiye Liang. Multigranulation decision-theoretic rough sets, International Journal of Approximate Reasoning, 2014, 55:225-237.
[60] Yuhua Qian, Hu Zhang, Feijiang Li, Qinghua Hu, Jiye Liang. Set-Based Granular Computing: a Lattice Model, International Journal of Approximate Reasoning, 2014, 55(3):834–852.
[61] 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.
[62] 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.
[63] 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.
[64] Guoping Lin, Jiye Liang, Yuhua Qian. Multigranulation rough sets: from partition to covering, Information Sciences, 2013, 241:101-118.
[65] Liang Bai, Jiye Liang, Chao Sui, Chuangyin Dang. Fast global k-means clustering based on local geometrical information, Information Sciences, 2013, 245:168–180.
[66] Liang Bai, Jiye Liang, Chuangyin Dang, Fuyuan Cao. A novel fuzzy clustering algorithm with between-cluster information for categorical data, Fuzzy Sets and Systems, 2013, 215:55–73.
[67] Liang Bai, Jiye Liang, Chuangyin Dang, Fuyuan Cao. The impact of cluster representatives on the convergence of the K-Modes type clustering, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(6):1509-1522.
[68] Feng Wang, Jiye Liang, Yuhua Qian. Attribute reduction: A dimension incremental strategy, Knowledge-Based Systems, 2013, 39:95-108.
[69] Feng Wang, Jiye Liang, Chuangyin Dang. Attribute reduction for dynamic data sets, Applied Soft Computing, 2013, 13(1):676-689.
[70] Fuyuan Cao, Jiye Liang, Deyu Li, Xingwang Zhao. A weighting k-Modes algorithm for subspace clustering of categorical data, Neurocomputing, 2013, 108:23-30.
[71] 高小方, 梁吉业. 基于等维度独立多流形的DC-ISOMAP算法, 计算机研究与发展, 2013, 50(8):1690~1699.
[72] 刘杨磊, 梁吉业, 高嘉伟, 杨静. 基于Tri-training的半监督多标记学习算法, 智能系统学报, 2013, 8(5):439-445.
[73] 李茹, 王智强, 李双红, 梁吉业, Collin Baker. 基于框架语义分析的汉语句子相似度计算, 计算机研究与发展, 2013, 50(8):1728~1736.
[74] Jiye Liang, Feng Wang, Chuangyin Dang, Yuhua Qian. An efficient rough feature selection algorithm with a multi-granulation view, International Journal of Approximate Reasoning, 2012, 53:912-926.
[75] Jiye Liang, Liang Bai, Chuangyin Dang, Fuyuan Cao. The k-means-type algorithms versus imbalanced data distributions, IEEE Transactions on Fuzzy Systems, 2012, 20(4):728-745.
[76] Jiye Liang, Ru Li, Yuhua Qian. Distance: a more comprehensible perspective for measures in rough set theory, Knowledge-Based Systems, 2012, 27:126-136.
[77] Jiye Liang, Xingwang Zhao, Deyu Li, Fuyuan Cao, Chuangyin Dang. Determining the number of clusters using information entropy for mixed data, Pattern Recognition, 2012, 45(6):2251–2265.
[78] Peng Song, Jiye Liang, Yuhua Qian. A two-grade approach to ranking interval data, Knowledge-Based Systems, 2012, 27:234-244.
[79] Wei Wei, Jiye Liang, Yuhua Qian. A comparative study of rough sets for hybrid data, Information Sciences, 2012, 190(5):1-16.
[80] Jifang Pang, Jiye Liang. Evaluation of the results of multi-attribute group decision-making with linguistic information, Omega, 2012, 40:294-301.
[81] Fuyuan Cao, Jiye Liang, Deyu Li, Liang Bai, Chuangyin Dang. A dissimilarity measure for the k-Modes clustering algorithm, Knowledge-Based Systems, 2012, 26:120-127.
[82] 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.
[83] Liang Bai, Jiye Liang, Chuangyin Dang, Fuyuan Cao. A cluster centers initialization method for clustering categorical data., Expert Systems with Applications, 2012, 39(9):8022-8029.
[84] Yuhua Qian, Jiye Liang, Weizhi Wu, Chuangyin Dang. Partial orderings of information granulations: a further investigation, Expert Systems, 2012, 29(1):3-24.
[85] Yuhua Qian, Jiye Liang, Wei Wei. Consistency-preserving attribute reduction in fuzzy rough set framework, International Journal of Machine Learning and Cybernetics, 2012, 2012:45-53.
[86] 白雪飞, 王文剑, 梁吉业. 基于区域显著性的活动轮廓分割模型, 计算机研究与发展, 2012, 49(12):2686-2695.
[87] Yuhua Qian, Jiye Liang, Weizhi Wu, Chuangyin Dang. Information granularity in fuzzy binary GrC model, IEEE Transactions on Fuzzy Systems, 2011, 19(2):253–264.
[88] Liang Bai, Jiye Liang, Chuangyin Dang, Fuyuan Cao. A novel attribute weighting algorithm for clustering high-dimensional categorical data, Pattern Recognition, 2011, 44(12):2843-2861.
[89] Liang Bai, Jiye Liang, Chuangyin Dang. An initialization method to simultaneously find initial cluster centers and the number of clusters for clustering categorical data, Knowledge-Based Systems, 2011, 24(6):785-795.
[90] Yuhua Qian, Jiye Liang, Witold Pedrycz, Chuangyin Dang. An efficient accelerator for attribute reduction from incomplete data in rough set framework, Pattern Recognition, 2011, 44(8):1658–1670.
[91] Fuyuan Cao, Jiye Liang. A data labeling method for clustering categorical data, Expert Systems with Applications, 2011, 38(3):2381-2385.
[92] Xiaofang Gao, Jiye Liang. The dynamical neighborhood selection based on the sampling density and manifold curvature for isometric data embedding, Pattern Recognition Letters, 2011, 32(2):202-209.
[93] 钱宇华, 梁吉业, 王锋. 面向非完备决策表的正向近似特征选择加速算法, 计算机学报, 2011, 34(3):435-442.
[94] 谭红叶, 郑家恒, 梁吉业. 时间关系识别研究进展, 中文信息学报, 2011, 25(5):44-52.
[95] 梁吉业, 白亮, 曹付元. 基于新的距离度量的K-Modes聚类算法, 计算机研究与发展, 2010, 47(10):1749-1755.
[96] Yuhua Qian, Jiye Liang, Yiyu Yao, Chuangyin Dang. MGRS: a mulit-granulation rough set, Information Sciences, 2010, 180:949-970.
[97] Yuhua Qian, Jiye Liang, Deyu Li, Feng Wang, Nannan Ma. Approximation reduction in inconsistent incomplete decision tables, Knowledge-Based Systems, 2010, 23(5):427-433.
[98] Yuhua Qian, Jiye Liang, Chuangyin Dang. Incomplete multigranulation rough set, IEEE Transactions on Systems, Man, and Cybernetics Part A, 2010, 40(2):420-431.
[99] Yuhua Qian, Jiye Liang, Witold Pedrycz, Chuangyin Dang. Positive approximation: an accelerator for attribute reduction in rough set theory, Artificial Intelligence, 2010, 174:597-618.
[100] 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.
[101] Fuyuan Cao, Jiye Liang, Liang Bai, Xingwang Zhao, Chuangyin Dang. A framework for clustering categorical time-evolving data, IEEE Transactions on Fuzzy Systems, 2010, 18(5):872-882.
[102] Yuhua Qian, Jiye Liang, Peng Song, Chuangyin Dang. On dominance relations in disjunctive set-valued ordered information systems, International Journal of Information Technology & Decision Making, 2010, 9(1):9-33.
[103] Hongxing Chen, Yuhua Qian, Jiye Liang, Wei Wei. On partial order relations in granular computing, GRC, 2010, 2010:102-106.
[104] Jiye Liang, Junhong Wang, Yuhua Qian. A new measure of uncertainty based on knowledge granulation for rough sets, Information Sciences, 2009, 17(9):458-470.
[105] Fuyuan Cao, Jiye Liang, Guang Jiang. An initialization method for the K-Means algorithm using neighborhood model, Computers and Mathematics with Applications, 2009, 58:474-483.
[106] Yuhua Qian, Jiye Liang, Chuangyin Dang. Knowledge structure, knowledge granulation and knowledge distance in a knowledge base, International Journal of Approximate Reasoning, 2009, 50:174-188.
[107] Yuhua Qian, Jiye Liang, Wei Wei. A new method for measuring the uncertainty in incomplete information systems, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2009, 17(6):855-880.
[108] Fuyuan Cao, Jiye Liang, Liang Bai. A new initialization method for categorical data clustering, Expert Systems with Applications, 2009, 36(7):10223-10228.
[109] 杨晓梅, 梁吉业, 曾建潮, 梁嘉骅. 基尼指数遗传算法求解具有共性的调度问题, 系统工程学报, 2009, 24(3):322-328.
[110] Yuhua Qian, Chuangyin Dang, Jiye Liang, Dawei Tang. Set-valued ordered information systems, Information Sciences, 2009, 179:2809-2832.
[111] 张剑妹, 陶世群, 梁吉业. XML结构完整性约束下的路径表达式的最小化, 软件学报, 2009, 20(11):2977-2987.
[112] Jianmei Zhang, Shiqun Tao, Jiye Liang. Logical inplication of structural integrity constraints for XML, Chinese Journal of Electronics, 2009, 18(2):243-248.
[113] Hongxing Chen, Yuhua Qian, Jiye Liang, Wei Wei, Feng Wang. A time-reduction stratejy to feature selection in rough set theory, , Lecture Notes in Computer Science, 2009, 2009:111-119.
[114] Jiye Liang, Yuhua Qian. Information granules and entropy theory in information systems, Science in China, Series F: Information Sciences, 2008, 51(10):1427-1444.
[115] 梁吉业, 钱宇华. 信息系统中的信息粒与熵理论, 中国科学(E辑:信息科学), 2008, 38(12):2048-2065.
[116] Jiye Liang, Baoli Wang, Yuhua Qian, Deyu Li. An algorithm of constructing maximal consistent block, International Journal of Computer Science and Knowledge Engineering, 2008, 2(1):11-18.
[117] 梁吉业, 褚成缘, 胡建龙, 李德玉. 科技项目完成情况的模糊综合评价研究, 系统工程学报, 2008, 23(5):636-640.
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1. 国家自然科学基金重点项目(No. ** ): 面向大数据的粒计算理论与方法, 2015.01－2019.12, (主持人)
2. 国家自然科学基金重点项目(No. ** ): 高维复杂数据分析理论及其在投资决策中的应用, 2011.01－2014.12, (主持人)
3. 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, (主持人)
4. 高等学校博士学科点专项科研基金(No. 002): 基于粒计算的符号数据分析方法研究, 2011.01-2013.12, (主持人)
5. 国家863计划项目(No. 2007AA01Z165): 面向高维复杂数据的粒度计算理论与算法研究, 2007.10－2009.12, (主持人)
6. 国家863计划项目(No. 2004AA115460): 专家系统及计算机软硬件系统评价技术研究, 2004.10－2005.12, (主持人)
7. 国家自然科学基金项目(No. ** ): 面向复杂数据的粗糙集多属性/多准则决策分析研究, 2010.01－2012.12, (主持人)
8. 国家自然科学基金项目(No. **): 复杂信息系统的粒度结构与知识获取研究, 2008.01－2010.12, (主持人)
9. 国家自然科学基金项目(No. **): 基于软计算技术的不确定性决策方法研究, 2005.01－2007.12, (主持人)
10. 国家自然科学基金项目(No. **): 粗糙集理论中的不确定性、模糊性与知识获取, 2003.01－2005.12, (主持人)
11. 高等学校博士学科点专项科研基金(No. ): 基于软计算技术的粒度计算理论和方法的研究, 2006.01－2008.12, (主持人)
12. 教育部科学技术研究重点项目: 基于粒度计算的不确定性信息处理的理论与方法研究, 2006.01－2008.12, (主持人)