最终学历：研究生
研究方向：数据挖掘与机器学习，粒度计算
电子邮箱：ljy@sxu.edu.cn
最终学位：博士
研究生导师：博士生导师
联系电话：0351-7010566
梁吉业：男，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副主任，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《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5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. 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
3. 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
4. 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
5. 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
6. 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
7. 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
8. 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
9. 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
10. 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
11. 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
12. 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
[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, DOI: 10.1109/TPAMI.2023.3298711
[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]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
[5]Yu Xie, Zhiguo Qin, Maoguo Gong, Bin Yu, Jiye Liang. Random deep graph matching[J]. IEEE Transactions on Knowledge & Data Engineering, 2023, 35(10): 10411-10422.
[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]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.
[8]Yuling Li , Kui Yu , Member, 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
[9]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
[10]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
[11]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. 2023. DOI: 10.1109/TNNLS.2022.3172879
[12]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, DOI: 10.1109/TBDATA.2023.3285477
[13] Qin Yue, Junbiao Cui, Liang Bai , Jianqing Liang, Jiye Liang. A zero-shot learning boosting framework via concept-constrained clustering[J]. Pattern Recognition, 2023,145: 109937.
[14] Jing Yan , Wei Wei, Xinyao Guo, Chuangyin Dang, Jiye Liang. A bi-level metric learning framework via self-paced learning weighting[J]. Pattern Recognition, 2023, 139: 109446.
[15] Liancheng He , Liang Bai , Xian Yang, Zhuomin Liang , Jiye Liang. Exploring the role of edge distribution in graph convolutional networks[J]. Neural Networks, 2023.
[16] Ting Guo, Jiye Liang, Guo-Sen Xie. Group-wise interactive region learning for zero-shot recognition[J]. Information Sciences, 2023, 642: 119135.
[17] Liancheng He, Liang Bai, Xian Yang, Hangyuan Du, Jiye Liang. High-order graph attention network[J]. Information Sciences, 2023, 630: 222-234.
[18] Lin Li, Yuze Li, Wei Wei , Yujia Zhang, Jiye Liang. Multi-actor mechanism for actor-critic reinforcement learning[J]. Information Sciences, 2023, 647: 119494.
[19] Xinyao Guo, Lin Li, Chuangyin Dang, Jiye Liang, Wei Wei. Multiple metric learning via local metric fusion[J]. Information Sciences, 2023, 621: 341-353.
[20] Cui Wentao, Liang Bai, Xian Yang, Jiye Liang. A new contrastive learning framework for reducing the effect of hard negatives[J]. Knowledge-Based Systems, 2023, 260: 110121.
[21] Jing Liu, Fuyuan Cao, Xuechun Jing, Jiye Liang.Deep multi-view graph clustering network with weighting mechanism and collaborative training[J]. Expert Systems with Applications, 2023, 236: 121298.
[22] Baoli Wang, Jiye Liang, Yiyu Yao. A trilevel analysis of uncertainty measuresin partition-based granular computing. Artifcial Intelligence Review, 2023,56(1): 533-575.
[1]Junbiao Cui, Jiye Liang. Fuzzy learning machine[C]. Advances in Neural Information Processing Systems, 2022, 35: 36693-36705.
[2]Liang Bai, Jiye Liang, Yunxiao Zhao. Self-constrained spectral clustering[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, 45(4): 5126-5138.
[3]Xinyan Liang, Yuhua Qian, Qian Guo, Honghong Cheng, Jiye Liang, AF: An Association-based Fusion Method for Multi-Modal Classification, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, 44(12):. 9236 - 9254
[4]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.
[5]Kaixuan Yao, Jiye Liang, Jianqing Liang, Ming Li, Feilong Cao. Multi-view graph convolutional networks with attention mechanism,Artificial Intelligence,2022, 307: 103708
[6]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.
[7]王克琪, 钱宇华, 梁吉业, 刘畅, 黄琴, 陈路, 贾洁茹. 局部-全局关系耦合的低照度图像增强. 中国科学:信息科学, 2022, 52(3): 443-460
[8]Feng Wang, Wei Wei, Jiye Liang. A group incremental approach for feature selection on hybrid data, Soft Computing, 2022, 26:3663–3677
[9]Kaihan Zhang, Zhiqiang Wang, Jiye Liang, Xingwang Zhao. A bayesian matrix factorization model for dynamic user embedding in recommender system, Frontiers of Computer Science, 2022, 16(5): 165346.
[10]刘晓琳, 白亮, 赵兴旺, 梁吉业. 基于多阶近邻融合的不完整多视图聚类算法. 软件学报, 2022, 33(4): 1354-1372.
[11]Anhui Tan, Xiaowan Ji, Jiye Liang, Yuzhi Tao, Wei-Zhi Wu, Witold Pedrycz. Weak multi-label learning with missing labels via instance granular discrimination, Information Sciences, 2022, 594:200-216.
[12]Liang Bai, Jiye Liang. A categorical data clustering framework on graph representation. Pattern Recognition, 2022, 128:108694.
[13]Ting Guo, Jianqing Liang, Jiye Liang, Guo-Sen Xie. Cross-modal propagation network for generalized zero-shot learning,Pattern Recognition Letters, 2022, 159:125-131.
[14]Xinyao Guo, Wei Wei, Jianqing Liang, Chuangyin Dang, Jiye Liang. Metric Learning via Perturbing Hard-to-classify Instances, Pattern Recognition, 2022, 132:108928.
[15]Anhui Tan, Jiye Liang, Weizhi Wu, Jia Zhang. Semi-supervised partial multi-label classification via consistency learning, Pattern Recognition,2022, 131:108839.
[16]Liang Bai, Yunxiao Zhao, Jiye Liang.Self-supervised spectral clustering with exemplar constraints, Pattern Recognition, 2022, 132: 108975.
[17]Jing Liu, Fuyuan Cao, Jiye Liang.Centroids-guided deep multi-view K-means clustering, Information Sciences, 2022: 876-896.
[18]Qingqiang Chen, Fuyuan Cao, Ying Xing, Jiye Liang. Instance selection: A bayesian decision theory perspective, In Proc. of the 36th AAAI Conf. on Artificial Intelligence (AAAI'22),online, Feb. 22-Mar. 1, 2022.
[19]Yunxia Wang, Fuyuan Cao, Kui Yu, Jiye Liang. Efficient causal structure learning from multiple interventional datasets with unknown targets, In Proc. of the 36th AAAI Conf. on Artificial Intelligence (AAAI'22), online, Feb. 22-Mar. 1, 2022.
[20]Wei Wei, Yujia Zhang, Jiye Liang, Lin Li, Yuze Li. Controlling underestimation bias in reinforcement learning via quasi-median operation, In Proc. of the 36th AAAI Conf. on Artificial Intelligence (AAAI'22), online, Feb. 22-Mar. 1, 2022.
[21]Zhihao Guo, Feng Wang, Kaixuan Yao, Jiye Liang, Zhiqiang Wang. Multi-scale variational graph autoencoder for link prediction. In Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining(WSDM '22), online,2022, 334–342.
[22]Qin Yue, Jiye Liang, Junbiao Cui, Liang Bai. Dual Bidirectional Graph Convolutional Networks for Zero-shot Node Classification. KDD 2022: 2408-2417
[23]Jie Wang, Jiye Liang, Kaixuan Yao, Jianqing Liang, Dianhui Wang. Graph convolutional autoencoders with co-learning of graph structure and node attributes, Pattern Recognition, 2022, 121,108215.
[24]Jiye Liang, Xiaolin Liu, Liang Bai, Fuyuan Cao, Dianhui Wang. Incomplete multi-view clustering via local and global co-regularization, SCIENCE CHINA Information Sciences, 2022, 65: 152105
[1]Liqin Yu,Fuyuan Cao, Xiao-Zhi Gao,Jing Liu,Jiye Liang, k-Mnv-Rep: a k-type clustering algorithm for matrix-object data, Information Sciences, 2021,542:40-57.
[2]Xingwang Zhao, Jiye Liang, Jie Wang, A community detection algorithm based on graph compression for large-scale social networks. Information Sciences, 2021, 551：358-372.
[3]Fuyuan Cao, Xiaolin Wu, Liqin Yu, Jiye Liang. An outlier detection algorithm for categorical matrix-object data, Applied Soft Computing, 2021, 104:107182.
[4]Gaoxia Jiang, Wenjian Wang, Yuhua Qian, Jiye Liang. A unified sample selection framework for output noise filtering: An error-bound perspective, Journal of Machine Learning Research, 2021,22(18):1−66.
[5]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
[6] 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
[7] Kaixuan Yao, Feilong Cao, Yee Leung, Jiye Liang. Deep neural network compression through interpretability-based filter pruning, Pattern Recognition, 2021, 119:108056
[8] JieWang, Jianqing Liang, Junbiao Cui, Jiye Liang. Semi-supervised learning with mixed-order graph convolutional networks, Information Sciences, 2021, 573: 171-181.
[9] Liang Bai,JiYe Liang,Fuyuan Cao,Semi-supervised clustering with constraints of different types from multiple information sources,IEEE Transactions on Pattern Analysis and Machine Intelligence,2021, 43(9):3247-3258.
[10] 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.
[11] 冯晨娇, 宋鹏, 梁吉业. 一种基于３因素概率图模型的长尾推荐方法. 计算机探究与发展, 2021, 58(9): 1975-1986.
[12] Qian Guo, Yuhua Qian,Xinyan Liang,Yanhong She, Deyu Li, Jiye Liang.Logic could be learned from images, International Journal of Machine Learning and Cybernetics, (2021) 12:3397–3414
[13] Xinyao Guo, Chuangyin Dang, Jianqing Liang, Wei Wei, Jiye Liang. Metric learning with clustering-based constraints, International Journal of Machine Learning and Cybernetics,2021,12:3597-3605.
[14] Anhui Tan, Jiye Liang, Wei-Zhi Wu, Jia Zhang, Lin Sun, Chao Chen. Fuzzy rough discrimination and label weighting for multi-label feature selection, Neurocomputing,2021, 465:128-140.
[1] Baoli Wang, Jiye Liang, Jifang Pang,Deviation degree: A perspective on score functions in hesitant fuzzy sets,International Journal of Fuzzy Systems,2019, 21(7): 2299-2317.
[2] Chao Zhang, Deyu Li,Jiye Liang, Multi-granularity three-way decisions with adjustable hesitant fuzzy linguistic multigranulation decision-theoretic rough sets over two universes, Information Sciences,2020, 507: 665-683.
[3] Chao Zhang, Deyu Li,Jiye Liang,Interval-valued hesitant fuzzy multi-granularity three-way decisions in consensus processes with applications to multi-attribute group decision making,Information Sciences,2020, 511: 192-211.
[4] Jifang Pang, Xiaoqiang Guan,Jiye Liang,Baoli Wang, Peng Song, Multi-attribute group decision-making method based on multi-granulation weights and three-way decisions,International Journal of Approximate Reasoning,2020,117:122-147.
[5] Yali Lv,Weixin Hu, Jiye Liang, Yuhua Qian, Junzhong Miao,A naive learning algorithm for class-bridge-decomposable multidimensional Bayesian network classifiers, Concurrency and Computation: Practice and Experience, 2020, DOI: 10.1002/cpe.5778.
[6] Chenjiao Feng, Jiye Liang, Peng Song, Zhiqiang Wang, A fusion collaborative filtering method for sparse data in recommender systems, Information Sciences, 2020, 521:365-379.
[7] Liqin Yu, Fuyuan Cao, Xingwang Zhao, Xiaodan Yang,Jiye Liang. Combining attribute content and label information for categorical data ensemble clustering, Applied Mathematics and Computation, 2020, 381:125280.
[8] 李飞江, 钱宇华, 王婕婷, 梁吉业, 王文剑. 基于样本稳定性的聚类方法. 中国科学：信息科学, 2020, 50(8): 1239-1254.
[9] 成红红, 钱宇华, 胡治国, 梁吉业. 基于邻域视角的关联关系挖掘方法. 中国科学：信息科学, 2020, 50(6): 824-844.
[10] JiYe Liang, Yunsheng Song, DeYu Li, Zhiqiang Wang, Chuangyin Dang.An accelerator for the logistic regression algorithm based on sampling on-demand,SCIENCE CHINA Information Sciences, 2020, 63(6): 169102.
[11] Liang Bai,JiYe Liang,Fuyuan Cao,A multiple k-means clustering ensemble algorithm to find nonlinearly separable clusters,Information Fusion,2020,61:36-47.
[12] Liang Bai, Junbin Wang, Jiye Liang, Hangyuan Du, New label propagation algorithm with pairwise constraints, Pattern Recognition, 2020,106,Article107411
[13] 孟银凤, 梁吉业. 线性正则化函数Logistic模型. 计算机研究与发展, 2020, 57(8): 1617-1626.
[14] Feilong Cao, Kaixuan Yao, Jiye Liang. Deconvolutional neural network for image super-resolution, Neural Networks, 2020, 132:394–404.
[15] Jing Liu,Fuyuan Cao,Xiao-Zhi Gao,Liqin Yu,Jiye Liang,A cluster-weighted kernel K-Means method for multi-view clustering,In Proc. of the 34th AAAI Conf. on Artificial Intelligence (AAAI'20),New York, NY, USA, Feb. 7-Feb. 12, 2020
[16] Liang Bai, Jiye Liang,A three-level optimization model for nonlinearly separable clustering, In Proc. of the 34th AAAI Conf. on Artificial Intelligence (AAAI'20),New York, NY, USA, Feb. 7-Feb. 12, 2020
[17] Liang Bai,Jiye Liang,Sparse subspace clustering with entropy-norm,Proceedings of the 37th International Conference on Machine Learning(ICML2020), Vienna, Austria,2020-07-12 - 2020-07-17
[1] Xingwang Zhao, Jiye Liang, Chuangyin Dang,A stratified sampling based clustering algorithm for large-scale data,Knowledge-Based Systems,2019,163:416–428.
[2] Wei Wei, Jiye Liang, Information fusion in rough set theory : An overview, Information Fusion, 2019, 48:107-118.
[3] 王智强, 梁吉业, 李茹. 基于信息融合的概率矩阵分解链路预测方法. 计算机研究与发展, 2019, 056(002):306-318.
[4] Anhui Tan, Weizhi Wu, Yuhua Qian, Jiye Liang, Jinkun Chen, Jinjin Li, Intuitionistic fuzzy rough set-based granular structures and attribute subset selection, IEEE Transactions on Fuzzy Systems, 2019, 27(3):527-539.
[5] Wei Wei,Jiye Liang, Xinyao Guo, Peng Song,Yijun Sun.Hierarchical division clustering framework for categorical data,Neurocomputing, 2019,341:118-134.
[6] Junfang Mu, Wenping Zheng, Jie Wang, Jiye Liang, A novel edge rewiring strategy for tuning structural properties in networks,Knowledge-Based Systems, 2019,177:55-67.
[7] Jie Wang, Jiye Liang, Wenping Zheng, Xingwang Zhao, Junfang Mu. Protein complex detection algorithm based on multiple topological characteristics in PPI networks. Information Sciences. 2019, 489:78-92.
[8] Junhong Wang, Shuliang Xu, Bingqian Duan, Caifeng Liu,Jiye Liang. An ensemble classification algorithm based on information entropy for data streams, Neural Processing Letters, 2019 50:2101-2117.
[9] Yunsheng Song,Jiye Liang,Feng Wang, An accelerator for support vector machines based on the local geometrical information and data partition,International Journal of Machine Learning and Cybernetics,2019, 10:2389–2400.
[10] 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.
[11] Liang Bai, Jiye Liang, Hangyuan Du, Yike Guo , An information-theoretical framework for cluster ensemble, IEEE Transactions on Knowledge and Data Engineering, 2019,31(8):1464-1477.
[1] 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.
[2] 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.
[3] Liang Bai,Jiye Liang,Hangyuan Du,YikeGuo. A novel community detection algorithm based on simplification of complex networks, Knowledge-Based Systems, 2018, 143：58–64.
[4] 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.
[5] Jiye Liang, Qianyu Shi, Xingwang Zhao, Multi-view data ensemble clustering: A cluster-level perspective,International Journal of Machine Intelligence and Sensory Signal Processing, 2018, 2(2):97-120.
[6] Chao Zhang, Deyu Li, Jiye Liang, Hesitant fuzzy linguistic rough set over two universes model and its applications, International Journal of Machine Learning and Cybernetics,2018, 9(4):577-588.
[7] Yuhua Qian, Xinyan Liang, Qi Wang, Jiye Liang, Bing Liu, Andrzej Skowron, et al. Local rough set: a solution to rough data analysis in big data, International Journal of Approximate Reasoning, 2018, 97:38-63.
[8] 胡清华, 王煜, 周玉灿, 赵红, 钱宇华, 梁吉业. 大规模分类任务的分层学习方法综述. 中国科学：信息科学, 2018, 48(5): 487-500.
[9] 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.
[10] Xingwang Zhao, Fuyuan Cao, Jiye Liang. A sequential ensemble clusterings generation algorithm for mixed data, Applied Mathematics and Computation, 2018, 335:264–277.
[11] Zhiqiang Wang,Jiye Liang,Ru Li. A fusion probability matrix factorization framework for link prediction, Knowledge-Based Systems, 2018,159:72-85.
[12] Yinfeng Meng,Jiye Liang,Fuyuan Cao,Yijun He. A new distance with derivative information for functional k-means clustering algorithm, Information Sciences, 2018, 463-464:166-185.
[13] 梁吉业, 乔杰, 曹付元, 刘晓琳. 面向短文本分析的分布式表示模型, 计算机研究与发展, 2018, 55(8):1631-1640.
[14] Fuyuan Cao,Joshua Zhexue Huang,Jiye Liang,Xingwang Zhao,Yinfeng Meng. An Algorithm for Clustering Categorical Data with Set-valued Features, IEEE Transactions on Neural Networks and Learning Systems, 2018,29(10):4593-4606.
[15] 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, 26(6):3524-3533.
[1]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.
[2]Yunsheng Song,Jiye Liang,Jing Lu,Xingwang Zhao. An efficient instance selection algorithm for k nearest neighbor regression, Neurocomputing, 2017, 251:26-34.
[3]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.
[4]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.
[5]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.
[6]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.
[7]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.
[8]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.
[9]Xingwang Zhao,Jiye Liang,Chuangyin Dang. Clustering ensemble selection for categorical data based on internal validity indices, Pattern Recognition, 2017, 69:150–168.
[10]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.
[11]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.
[12]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.
[13]Yu Wang, Qinghua Hu, Yucan Zhou, Hong Zhao, Yuhua Qian, Jiye Liang.Local bayes risk minimization based stopping strategy for hierarchical classification,2017 IEEE International Conference on Data Mining(ICDM), 2017, 515-524.
[1]Yanli Sang,Jiye Liang,Yuhua Qian. Decision-theoreticroughsetsunderdynamicgranulation, Knowledge-Based Systems, 2016, 91:84-92.
[2]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.
[3]Yinfeng Meng,Jiye Liang,Yuhua Qian. Comparison study of orthonormal representations of functional data in classification, Knowledge-Based Systems, 2016, 97:224–236.
[4]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.
[5]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.
[6]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.
[7]Feng Wang,Jiye Liang. An efficient feature selection algorithm for hybrid data, Neurocomputing, 2016, 193:33–41.
[8]Wei Wei,Junbiao Cui,Jiye Liang,Junhong Wang. Fuzzy rough approximations for set-valued data, Information Sciences, 2016, 360:181–201.
[9]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.
[10]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.
[11]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.
[1]Yuhua Qian, Qi Wang, Honghong Cheng, Jiye Liang, Chuangyin Dang. Fuzzy-rough feature selection accelerator, Fuzzy Sets and Systems,2015, 258: 61–78.
[2]Guoping Lin, Jiye Liang, Yuhua Qian, Uncertainty measures for multigranulation approximation space, International Journal of Uncertianty, Fuzziness and Knowledge-Based Systems, 2015, 23(3):443–457.
[3]Xiaofang Gao,Jiye Liang. An improved incremental nonlinear dimensionality reduction for isometric data embedding, Information Processing Letters, 2015, 115(4):492–501.
[4]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.
[5]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.
[6]Guoping Lin,Jiye Liang,Yuhua Qian. An information fusion approach by combining multigranulation rough sets and evidence theory, Information Sciences, 2015, 314:184–199.
[7]Wei Wei,Junhong Wang,Jiye Liang,Xin Mi,Chuangyin Dang. Compacted decision tables based attribute reduction, Knowledge-Based Systems, 2015, 86:261-277.
[8]Liang Bai,Jiye Liang. Cluster validity functions for categorical data:a solution-space perspective, Data Mining and Knowledge Discovery,2015,29(6):1560-1597.
[9]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.
[10]Yuhua Qian,Jiye Liang,Chuangyin Dang. Fuzzy granular structure distance, IEEE Transactions on Fuzzy Systems, 2015, 23(6):2245-2259.
[11]Jiye Liang,Decision-oriented rough set methods, 15th International Conference, RSFDGrC, 2015, 3-12.
Top↑ [1]Fuyuan Cao, Joshua Zhexue Huang, Jiye Liang. Trend analysis of categorical data streams with a concept change method, Information Sciences, 2014, 276:160-173.
[2]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.
[3]Liang Bai, Jiye Liang. The k-modes type clustering plus between-cluster information for categorical data, Neurocomputing, 2014, 133: 111–121.
[4]Yuhua Qian, Hu Zhang, Yanli Sang, Jiye Liang. Multigranulation decision-theoretic rough sets, International Journal of Approximate Reasoning, 2014,55:225-237.
[5]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.
[6]Baoli Wang, Jiye Liang, Yuhua Qian.Preorder information based attributes weights learning in multi-attribute decision making, Fundamenta Informaticae,2014,132:331-347.
[7]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.
[1]Feng Wang, Jiye Liang, Chuangyin Dang. Attribute reduction for dynamic data sets, Applied Soft Computing, 2013, 13(1):676-689.
[2]Feng Wang, Jiye Liang, Yuhua Qian. Attribute reduction: A dimension incremental strategy, Knowledge-Based Systems, 2013,39:95-108.
[3]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.
[4]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.
[5]Fuyuan Cao,Jiye Liang,Deyu Li,Xingwang Zhao. A weighting k-Modes algorithm for subspace clustering of categorical data, Neurocomputing, 2013, 108:23-30.
[6]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.
[7]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.
[8]Guoping Lin, Jiye Liang, Yuhua Qian. Multigranulation rough sets: from partition to covering, Information Sciences, 2013,241:101-118.
[9]Liang Bai, Jiye Liang, Chao Sui, Chuangyin Dang, Fast global k-means clustering based on local geometrical information, Information Sciences,2013,245: 168–180.
[10]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.
[11]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.
[1]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.
[2]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.
[3]Peng Song, Jiye Liang, Yuhua Qian. A two-grade approach to ranking interval data. Knowledge-Based Systems, 2012,27:234-244.
[4]Wei Wei, Jiye Liang, Yuhua Qian. A comparative study of rough sets for hybrid data. Information Sciences, 2012, 190:1-16.
[5]Yuhua Qian, Jiye Liang, Weizhi Wu, Chuangyin Dang. Partial orderings of information granulations: a further investigation. Expert Systems, 2012, 29(1):3-24.
[6]Jifang Pang, Jiye Liang. Evaluation of the results of multi-attribute group decision-making with linguistic information, Omega, 2012, 40: 294-301.
[7]Jiye Liang, Xingwang Zhao, Deyu Li, Fuyuan Cao, Chuangyin Dang, Determining the number of clusters using information entropy for mixed data. Pattern Recognition,2012, 45:2251–2265.
[8]Jiye Liang, Ru Li, Yuhua Qian. Distance: a more comprehensible perspective for measures in rough set theory. Knowledge-Based Systems, 2012, 27:126-136.
[9]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.
[10]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.
[11]Yuhua Qian, Jiye Liang, Weiwei. Consistency-preserving attribute reduction in fuzzy rough set framework. International Journal of Maching Learning and Cybernetics, 2012, 2012:45-53.
[12]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.
[13]Baoli Wang, Jiye Liang, Yuhua Qian. Information granularity and granular structure in decision making, RSKT, 2012: 440-449.
[14]Wei Wei, Jiye Liang, Yuhua Qian, Feng Wang. Variable precision multi-granulation rough set. GrC,2012: 639-643.
[15]Jiye Liang. Feature selection for large-scale data sets in GrC. GrC, 2012: 2-7.
[1]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.
[2]Fuyuan Cao, Jiye Liang. A data labeling method for clustering categorical data. Expert Systems with Applications, 2011,38(3): 2381-2385.
[3]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.
[4]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.
[5]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.
[6]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.
[7]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.
[8]Junhong Wang, Jiye Liang, Yuhua Qian. Closed-label concept lattice based rule extraction approach. ICIC (3) 2011: 690-698.
[9]Yuhua Qian, Chao Li, Jiye Liang. An efficient fuzzy-rough attribute reduction approach. RSKT 2011: 63-70.
[10]Yuhua Qian, Jiye Liang. How to organize data with measurement errors? SMC 2011: 3096-3101.
[11]Jiye Liang. Uncertainty and feature selection in rough set theory. RSKT 2011: 8-15.
[1]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.
[2]Yuhua Qian, Jiye Liang, Chuangyin Dang. Incomplete multigranulation rough set. IEEE Trasactions on Systems, Man and Cybernetics-Part A, 2010, 40(2):420-431.
[3]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.
[4]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.
[5]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.
[6]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.
[7]Yuhua Qian, Jiye Liang, Yiyu Yao, Chuangyin Dang. MGRS: a mulit-granulation rough set. Information Sciences, 2010, 180: 949-970.
[8]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.
[1]Xiaomei Yang, Jiye Liang, Jianchao Zeng, Jiahua Liang. Gini-index genetic algorithm for the scheduling problems with similar characteristics. Journal of Systems Engineering, 2009, 24(3): 322-328.
[2]Yuhua Qian, Jiye Liang, Feng Wang. 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.
[3]Fuyuan Cao, Jiye Liang, Liang Bai. A new initialization method for categorical data clustering. Expert Systems with Applications,2009,36(7): 10223-10228.
[4]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.
[5]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.
[6]Yuhua Qian, Chuangyin Dang, Jiye Liang, Dawei Tang. Set-valued ordered information systems. Information Sciences, 2009, 179 : 2809-2832.
[7]Xiaomei Yang, Jianchao Zeng, Jiye Liang. Apply inversion order number genetic algorithm to the job shop scheduling problem. WGEC 2009: 196-200.
[8]Hongxing Chen, Yuhua Qian, Jiye Liang, Wei Wei, Feng Wang. A time-reduction strategy to feature selection in rough set theory. RSKT 2009: 111-119.
[9]Wei Wei, Jiye Liang, Yuhua Qian, Feng Wang. An attribute reduction approach and its accelerated version for hybrid data. IEEE ICCI 2009: 167-173.
[1]Jiye Liang, Baoli Wang, Yuhua Qian, Deyu Li. An algorithm of constructing maximal consistent block. International Journal of Computer Science and Knowledge Engineering, 2(1) (2008) 11-18.
[2]Yuhua Qian, Jiye Liang. Positive approximation and rule extracting in incomplete information systems. International Journal of Computer Science and Knowledge Engineering, 2008, 2(1) : 51-63.
[3]Yuhua Qian, Jiye Liang, Deyu Li, Haiyun Zhang, Chuangyin Dang. Measures for evaluating the decision performance of a decision table in rough set theory. Information Sciences, 2008, 178(1): 181-202.
[4]Junhong Wang, Jiye Liang, Yuhua Qian, Chuangyin Dang. Uncertainty measure of rough sets based on a knowledge granulation of incomplete information systems. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems. 2008, 16(2): 233-244.
[5]Yuhua Qian, Jiye Liang. Combination Entropy & Combination Granulation in Rough Set Theory. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems. 2008, 16(2): 179-193.
[6]Yuhua Qian, Jiye Liang, Chuangyin Dang, Haiyun Zhang, Jianmin Ma. On the evaluation of the decision performance of an incomplete decision table. Data & Knowledge Engineering. 2008, 65(3):373-400.
[7]梁吉业,魏巍,钱宇华. 一种基于条件熵的增量核求解方法. 系统工程理论与实践, 2008, 4:81-89.
[8]Yuhua Qian，Chuangyin Dang, Jiye Liang, Feng Wang, Wei Xu. Knowledge distance in information systems. Journal of System Sciences and System Engineering, 2007, 16(4): 434-449.
[9]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.
[10]Yuhua Qian, Jiye Liang, Chuangyin Dang. Converse approximation and rule extracting from decision tables in rough set theory. Computers & Mathematics with Applications. 2008, 55: 1754-1765.
[11]Yuhua Qian, Jiye Liang, Chuangyin Dang. Consistency measure, inclusion degree and fuzzy measure in decision tables. Fuzzy Sets and Systems, 2008, 159: 2353-2377.
[12]Yuhua Qian, Jiye Liang, Chuangyin Dang. Interval ordered information systems. Computers & Mathematics with Applications, 2008, 56: 1994-2009.
[13]Jiye Liang, Yuhua Qian. Information granules and entropy theory in information systems. Science in China, Series F: Information Sciences, 2008, 51(10) : 1427-1444.
[14]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.
[15]Yuhua Qian, Jiye Liang, Wei Wei, Feng Wang. Consistency and fuzziness in ordered decision tables. RSKT 2008: 63-71.
[16]Wei Wei, Jiye Liang, Yuhua Qian. Change mechanism of a decision table's decision performance caused by attribute reductions. GrC 2008: 638-643.
[17]Yuhua Qian, Jiye Liang, Wei Wei. Granulation operators on a knowledge base. GrC 2008: 538-543.
[1] Kaishe Qu, Yanhui Zhai, Jiye Liang. Study of decision implications based on formal concept analysis. International Journal of General Systems, 2007, 36(2), 147-156.
[2] 曲开社, 翟岩慧, 梁吉业, 李德玉. 形式概念分析对粗糙集理论的表示及扩展. 软件学报, 2007, 18(9): 2174-2182.
[3] Yuhua Qian, Jiye Liang. Evaluation method for decision rule sets. RSFDGrC 2007: 272-279.
[4] Yuhua Qian, Jiye Liang, Chuangyin Dang. MGRS in incomplete information systems. GrC 2007: 163-168.
[1] Jiye Liang, Zhongzhi Shi, Deyu Li, M. J. Wireman. The information entropy, rough entropy and knowledge granulation in incomplete information systems. International Journal of General Systems, 2006, 34(1): 641-654.
[2] Jiye Liang, Jifang Pang. A measure method for indiscernibility in imperfect information system. JCIS 2006.
[3] Yuhua Qian, Jiye Liang. Combination entropy and combination granulation in incomplete information system. RSKT 2006: 184-190.
[1] Jiye Liang, Yuhua Qian, Chengyuan Chu, Deyu Li, Junhong Wang. Rough set approximation based on dynamic granulation. RSFDGrC 2005: 701-708.
[1] Jiye Liang, Zhongzhi Shi. The information entropy, rough entropy and knowledge granulation in rough set theory. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2004, 12 (1) : 37-46.
[2] Kaishe Qu, Jiye Liang, Junhong Wang, Zhongzhi Shi. The algebraic properties of concept lattice. Journal of Systems Science and Information, 2004, 3 (2): 36-47.
[3] 梁吉业, 王俊红. 基于概念格的规则产生集挖掘算法. 计算机研究与发展, 2004, 41(8): 1339-1344.
Top↑ [1] Jiye Liang, Zhongzhi Shi, Deyu Li. Applications of inclusion degree in rough set theory. International Journal of Computational Cognition, 2003, 1 (2): 67-78.
[2] 曹飞龙, 徐宗本, 梁吉业. 多项式函数的神经网络逼近:网络的构造与逼近算法. 计算机学报, 2003, 26(8): 906-912.
[3] K. S. Chin, Jiye Liang, Chuangyin Dang. Rough set data analysis algorithms for incomplete information systems. RSFDGrC 2003: 264-268.
[1]Jiye Liang, Zongben Xu. The algorithm on knowledge reduction in incomplete information systems. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2002, 10 (1): 95-103.
[2] Zongben Xu, Jiye Liang, Chuanyin Dang, K. S. Chin．Inclusion degree: a perspective on measures for rough set data analysis. Information Sciences, 2002, 141 (3-4): 229-238.
[3]Jiye Liang, K. S. Chin, Chuangyin Dang, C. M. YAM. Richard. A new method for measuring uncertainty and fuzziness in rough set theory. International Journal of General Systems, 2002, 31(4): 331-342.
[1]Jiye Liang, Kaishe Qu, Zongben Xu. Reduction of attribute in information systems. Systems Engineering-Theory & Practice, 2001,12: 76-80.
[2] 梁吉业, 徐宗本, 李月香. 包含度与粗糙集数据分析中的度量. 计算机学报,2001, 24(5): 544-547.
[3] Jiye Liang, Kaishe Qu．Information measures of roughness of knowledge and rough sets in incomplete information systems. Journal of System Science and System Engineering, 2001, 10(4): 418-424.