Journal Papers

2022 (18 篇)
[18] Jing Liu, Fuyuan Cao, Jiye Liang.Centroids-guided deep multi-view K-means clustering, Information Sciences, 2022: 876-896.
[17] Liang Bai, Yunxiao Zhao, Jiye Liang.Self-supervised spectral clustering with exemplar constraints, Pattern Recognition, 2022, 132: 108975.
[16] Anhui Tan, Jiye Liang, Weizhi Wu, Jia Zhang. Semi-supervised partial multi-label classification via consistency learning, Pattern Recognition,2022, 131:108839.
[15] Xinyao Guo, Wei Wei, Jianqing Liang, Chuangyin Dang, Jiye Liang. Metric Learning via Perturbing Hard-to-classify Instances, Pattern Recognition, 2022, 132:108928.
[14] Liang Bai,Jiye Liang,Yunxiao Zhao. Self-constrained spectral clustering, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, Doi:10.1109/TPAMI.2022.3188160.
[13] 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
[12] 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.
[11] Liang Bai, Jiye Liang. A categorical data clustering framework on graph representation. Pattern Recognition, 2022, 128:108694. PDF
[10] Baoli Wang, Jiye Liang, Yiyu Yao. A trilevel analysis of uncertainty measuresin partition-based granular computing. Artifcial Intelligence Review, 2022, https://doi.org/10.1007/s10462-022-10177-6. PDF
[9] Kaixuan Yao, Jiye Liang, Jianqing Liang, Ming Li, Feilong Cao. Multi-view graph convolutional networks with attention mechanism,Artificial Intelligence,2022, 307: 103708 PDF
[8] 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, doi: 10.1109/TPAMI.2021.3125995. PDF
[7] 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. PDF
[6] Xiaolin Liu, Liang Bai, Xingwang Zhao, Jiye Liang. Incomplete multi-view clustering algorithm based on multi-order neighborhood diffusion and fusion, Journal of Software, 2022, 33(4):1354−1372. (in Chinese) PDF
[5] 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, Doi: 10.1007/s11432-020-3369-8. PDF
[4] 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. PDF
[3] Feng Wang, Wei Wei, Jiye Liang. A group incremental approach for feature selection on hybrid data, Soft Computing, 2022, 26:3663–3677 PDF
[2] Keqi Wang, Yuhua Qian, Jiye Liang, Chang Liu, Qin Huang, Lu Chen, Jieru jia, Local-global coupling relationship based low-light image enhancement, SCIENTIA SINICA Informationis, 2022, 52(3): 443-460.(in Chinese) PDF
[1] 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. PDF
2021 (14 篇)
[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. PDF
[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. PDF
[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 PDF
[11] Chenjiao Feng, Peng Song, Zhiqiang Wang, Jiye Liang. A method on long tail recommendation based on three-factor probabilistic graphical model, Journal of Computer Research and Development, 2021, 58(9):1975-1986.(in Chinese) PDF
[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. PDF
[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. PDF
[8] JieWang, Jianqing Liang, Junbiao Cui, Jiye Liang. Semi-supervised learning with mixed-order graph convolutional networks, Information Sciences, 2021, 573: 171-181. PDF
[7] Kaixuan Yao, Feilong Cao, Yee Leung, Jiye Liang. Deep neural network compression through interpretability-based filter pruning, Pattern Recognition, 2021, 119:108056 PDF
[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 PDF
[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 PDF
[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. PDF
[3] Fuyuan Cao, Xiaolin Wu, Liqin Yu, Jiye Liang. An outlier detection algorithm for categorical matrix-object data, Applied Soft Computing, 2021, 104:107182. PDF
[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. PDF
[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. PDF
2020 (14 篇)
[14] Feilong Cao, Kaixuan Yao, Jiye Liang. Deconvolutional neural network for image super-resolution, Neural Networks, 2020, 132:394–404. PDF
[13] Yinfeng Meng,Jiye Liang, Linear regularized functional logistic model,Journal of Computer Research and Development, 2020, 57(8): 1617-1626.(In Chinese) PDF
[12] Liang Bai, Junbin Wang, Jiye Liang, Hangyuan Du, New label propagation algorithm with pairwise constraints, Pattern Recognition, 2020,106,Article107411 PDF
[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. PDF
[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. PDF
[9] Honghong Cheng, Yuhua Qian, Zhiguo Hu, Jiye Liang. Association mining method based on neighborhood, Scientia Sinica Informationis, 2020, 50(6):824-844.(In Chinese) PDF
[8] Feijiang Li, Yuhua Qian, Jieting Wang, Jiye Liang, Wenjian Wang. Clustering method based on sample's stability,Scientia Sinica Informationis, 2020, 50(8):1239-1254.(In Chinese) PDF
[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. PDF
[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. PDF
[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. PDF
[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. PDF
[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 PDF
[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. PDF
[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. PDF
2019 (11 篇)
[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. PDF
[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. PDF
[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 PDF
[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. PDF
[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. PDF
[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. PDF
[5] Wei Wei,Jiye Liang, Xinyao Guo, Peng Song,Yijun Sun.Hierarchical division clustering framework for categorical data,Neurocomputing, 2019,341:118-134. PDF
[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. PDF
[3] Zhiqiang Wang , Jiye Liang, Ru Li. Probability matrix factorization for link prediction based on information fusion. Journal of Computer Research and Development, 2019, 56(2): 306-318.(In Chinese) PDF
[2] Wei Wei, Jiye Liang, Information fusion in rough set theory : An overview, Information Fusion, 2019, 48:107-118. PDF
[1] Xingwang Zhao, Jiye Liang, Chuangyin Dang,A stratified sampling based clustering algorithm for large-scale data,Knowledge-Based Systems,2019,163:416–428. PDF
2018 (15 篇)
[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. PDF
[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 PDF
[13] Jiye Liang, Jie Qiao, Fuyuan Cao, Xiaolin Liu, A distributed representation model for short text analysis, Journal of Computer Research and Development, 2018, 55(8): 1631-1640.(in Chinese) PDF
[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. PDF
[11] Zhiqiang Wang,Jiye Liang,Ru Li. A fusion probability matrix factorization framework for link prediction, Knowledge-Based Systems, 2018,159:72-85. PDF
[10] Xingwang Zhao, Fuyuan Cao, Jiye Liang. A sequential ensemble clusterings generation algorithm for mixed data, Applied Mathematics and Computation, 2018, 335:264–277. PDF
[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. PDF
[8] Qinghua Hu, Yu Wang, Yucan Zhou, Hong Zhao, Yuhua Qian, Jiye Liang. Review on hierarchical learning methods for large-scale classification task, Scientia Sinica Informationis, 2018, 48(5):487-500.(In Chinese) PDF
[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 PDF
[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. PDF
[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. PDF
[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. PDF
[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. PDF
[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. PDF
[1] Zhang Kaihan, Liang Jiye, Zhao Xingwang,Wang Zhiqiang. A collaborative filtering recommendation algorithm based on information of community experts. Journal of Computer Research and Development, 2018, 55(5): 968-976.(In Chinese) PDF
2017 (12 篇)
[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. PDF
[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. PDF
[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. PDF
[9] Xingwang Zhao,Jiye Liang,Chuangyin Dang. Clustering ensemble selection for categorical data based on internal validity indices, Pattern Recognition, 2017, 69:150–168. PDF
[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. PDF
[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 PDF
[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. PDF
[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. PDF
[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. PDF
[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. PDF
[2] Yunsheng Song,Jiye Liang,Jing Lu,Xingwang Zhao. An efficient instance selection algorithm for k nearest neighbor regression, Neurocomputing, 2017, 251:26-34. PDF
[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. PDF
2016 (11 篇)
[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. PDF
[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. PDF
[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. PDF
[8] Wei Wei,Junbiao Cui,Jiye Liang,Junhong Wang. Fuzzy rough approximations for set-valued data, Information Sciences, 2016, 360:181–201 PDF
[7] Feng Wang,Jiye Liang. An efficient feature selection algorithm for hybrid data, Neurocomputing, 2016, 193:33–41. PDF
[6] Xingwang Zhao, Jiye Liang. An attribute weighted clustering algorithm for mixed data based on information entropy, Journal of Computer Research and Development, 2016, 53(5): 1018-1028.(in Chinese) PDF
[5] Jiye Liang, Chenjiao Feng, Peng Song. A survey on correlation analysis of big data.Chinese Journal of Computers，2016, 39(1):1-18.(in Chinese) PDF
[4] Zhiqiang Wang, Ru Li,Jiye Liang,Xuhua Zhang,Juan Wu,Na Su，Research on question answering for reading comprehension based on Chinese discourse frame semantic parsing,Chinese Journal of Computers,2016, 39(4):795-807. (in Chinese) PDF
[3] Yinfeng Meng,Jiye Liang,Yuhua Qian. Comparison study of orthonormal representations of functional data in classification, Knowledge-Based Systems, 2016, 97:224–236. PDF
[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. PDF
[1] Yanli Sang,Jiye Liang,Yuhua Qian. Decision-theoreticroughsetsunderdynamicgranulation, Knowledge-Based Systems, 2016, 91:84-92. PDF
2015 (10 篇)
[10] Yuhua Qian,Jiye Liang,Chuangyin Dang. Fuzzy granular structure distance, IEEE Transactions on Fuzzy Systems, 2015, 23(6):2245-2259. PDF
[9] Jiye Liang, Yuhua Qian, Deyu Li, Qinghua Hu.Theory and method of granular computing for big data mining, Science in China-Series F: Information Sciences ,2015, 45(11):1355-1369.（in Chinese） PDF
[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. PDF
[7] Wei Wei,Junhong Wang,Jiye Liang,Xin Mi,Chuangyin Dang. Compacted decision tables based attribute reduction, Knowledge-Based Systems, 2015, 86:261-277. PDF
[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. PDF
[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 PDF
[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. PDF
[3] Xiaofang Gao,Jiye Liang. An improved incremental nonlinear dimensionality reduction for isometric data embedding, Information Processing Letters, 2015, 115(4):492–501. PDF
[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 PDF
[1] Yuhua Qian, Qi Wang, Honghong Cheng, Jiye Liang, Chuangyin Dang. Fuzzy-rough feature selection accelerator, Fuzzy Sets and Systems,2015, 258: 61–78. PDF
2014 (8 篇)
[8] 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. PDF
[7] Baoli Wang, Jiye Liang, Yuhua Qian.Preorder information based attributes weights learning in multi-attribute decision making, Fundamenta Informaticae,2014,132:331-347. PDF
[6] 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. PDF
[5] Yuhua Qian, Hu Zhang, Yanli Sang, Jiye Liang. Multigranulation decision-theoretic rough sets, International Journal of Approximate Reasoning, 2014,55:225-237. PDF
[4] Yuhua Qian, Hu Zhang, Yanli Sang, Jiye Liang. Multigranulation decision-theoretic rough sets, International Journal of Approximate Reasoning, 2014,55:225-237. PDF
[3] Liang Bai, Jiye Liang. The k-modes type clustering plus between-cluster information for categorical data, Neurocomputing, 2014, 133: 111–121. PDF
[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 . PDF
[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. PDF
2013 (11 篇)
[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. PDF
[10] Xiaofang Gao,Jiye Liang. Manifold learning algorithm DC-ISOMAP of data lying on the well-separated multi-manifold with same intrinsic dimension.Journal of Computer Research and Development, 50(8):1690-1699. PDF
[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. PDF
[8] Guoping Lin, Jiye Liang, Yuhua Qian. Multigranulation rough sets: from partition to covering, Information Sciences, 2013,241:101-118. PDF
[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 PDF
[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. PDF
[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. PDF
[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. PDF
[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 PDF
[2] Feng Wang, Jiye Liang, Yuhua Qian. Attribute reduction: A dimension incremental strategy, Knowledge-Based Systems, 2013,39:95-108. PDF
[1] Feng Wang, Jiye Liang, Chuangyin Dang. Attribute reduction for dynamic data sets, Applied Soft Computing, 2013, 13(1):676-689. PDF
2012 (12 篇)
[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. PDF
[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. PDF
[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. PDF
[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. PDF
[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. PDF
[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. PDF
[6] Jifang Pang, Jiye Liang. Evaluation of the results of multi-attribute group decision-making with linguistic information, Omega, 2012, 40: 294-301. PDF
[5] Yuhua Qian, Jiye Liang, Weizhi Wu, Chuangyin Dang. Partial orderings of information granulations: a further investigation. Expert Systems, 2012, 29(1):3-24. PDF
[4] Wei Wei, Jiye Liang, Yuhua Qian. A comparative study of rough sets for hybrid data. Information Sciences, 2012, 190:1-16. PDF
[3] Peng Song, Jiye Liang, Yuhua Qian. A two-grade approach to ranking interval data. Knowledge-Based Systems, 2012,27:234-244. PDF
[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. PDF
[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. PDF
2011 (7 篇)
[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. PDF
[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. PDF
[5] Yuhua Qian, Jiye Liang, Feng Wang. A positive approximation based accelerated algorithm to feature selection from incomplete decision tables. Journal of Computer, 2011, 34(3), 435-442. PDF
[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. PDF
[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. PDF
[2] Fuyuan Cao, Jiye Liang. A data labeling method for clustering categorical data. Expert Systems with Applications, 2011,38(3): 2381-2385. PDF
[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. PDF
2010 (8 篇)
[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. PDF
[7] Yuhua Qian, Jiye Liang, Yiyu Yao, Chuangyin Dang. MGRS: a mulit-granulation rough set. Information Sciences, 2010, 180: 949-970. PDF
[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. PDF
[5] Jiye Liang, Liang Bai, Fuyuan Cao. K-modes clustering algorithm based on a new distance measure. Journal of Computer Research and Development, 2010, 47(10): 1749-1755. PDF
[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. PDF
[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. PDF
[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. PDF
[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. PDF
2009 (7 篇)
[7] Yuhua Qian, Chuangyin Dang, Jiye Liang, Dawei Tang. Set-valued ordered information systems. Information Sciences, 2009, 179 : 2809-2832. PDF
[6] 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. PDF
[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. PDF
[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. PDF
[3] Fuyuan Cao, Jiye Liang, Liang Bai. A new initialization method for categorical data clustering. Expert Systems with Applications,2009,36(7): 10223-10228. PDF
[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. PDF
[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. PDF
2008 (14 篇)
[14] Jiye Liang, Yuhua Qian. Information granules and entropy theory in information systems. Science in China, Series E: Information Sciences, 2008, 38(12) : 2048-2065.(in Chinese) PDF
[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. PDF
[12] Yuhua Qian, Jiye Liang, Chuangyin Dang. Interval ordered information systems. Computers & Mathematics with Applications, 2008, 56: 1994-2009. PDF
[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. PDF
[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. PDF
[9] Jiye Liang, Chengyuan Zhu, Jianlong Hu, Deyu Li. Research on fuzzy integrative evaluation for implemented situation of technological projects. Journal of Systems Engineering, 2008, 23(5): 636-640. PDF
[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. PDF
[7] Jiye Liang, Wei Wei, Yuhua Qian. An incremental approach to computation of a core based on conditional entropy. Systems Engineering-Theory & Practice, 2008, 4: 81-89. PDF
[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. PDF
[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. PDF
[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. PDF
[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. PDF
[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. PDF
[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. PDF
2007 (2 篇)
[2] Kaishe Qu, Yanhui Zhai, Jiye Liang, Deyu Li. Representation and extension of rough set theory Based on formal concept analysis. Journal of Software, 2007, 18(9): 2174-2182. PDF
[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. PDF
2006 (1 篇)
[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. PDF
2004 (3 篇)
[3] Jiye Liang, Junhong Wang. An algorithm for extracting rule-generating sets based on concept lattice. Journal of Computer Research and Development, 2004, 41(8): 1339-1344. PDF
[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. PDF
[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. PDF
2003 (2 篇)
[2] Feilong Cao, Zongben Xu, Jiye Liang. Approximation of polynomial functions by neural network: construction of network and algorithm of approximation. Chinese Journal of Computers, 2003, 26(8): 906-912. PDF
[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. PDF
2002 (3 篇)
[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 PDF
[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. PDF
[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. PDF
2001 (3 篇)
[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. PDF
[2] Jiye Liang, Zongben Xu, Yuexiang Li. Inclusion degree and measures of rough set data analysis. Chinese Journal of Computers,2001, 24(5): 544-547. PDF
[1] Jiye Liang, Kaishe Qu, Zongben Xu. Reduction of attribute in information systems. Systems Engineering-Theory & Practice, 2001,12: 76-80. PDF