何清

2008年10月被中國科學院計算技術研究所聘為研究員

2007年6月被中國科學院計算技術研究所聘為博士生導師

2006年5月被中國科學院研究生院聘為教授

2002年8月，在中國科學院計算技術研究所，副研究員，碩士生導師

2000年8月-2002年8月 中國科學院智能信息處理重點實驗室博士後

1997年8月-2000年7月 北京師範大學 模糊數學與人工智能專業 博士畢業，獲博士學位

1996年7月被河北科技大學評聘為副教授

1987年8月-1997年7月 河北科技大學教師

1987年8月鄭州大學數學專業碩士研究生畢業

1985年8月河北師範大學數學系本科畢業

2001年11月俄羅斯聖彼得堡信息與自動化研究所合作交流,執行中俄政府間科技合作項目

2003年10月澳大利亞UniSA高級訪問學者，執行中澳國際特別基金合作項目

2004年10月澳大利亞UTS, 中國科學院高級訪問學者計劃.

2014-04-25-今,中國電子學會大數據專家委員會, 委員

2012-05-31-今,中國通信學會大數據專家委員會, 委員

2009-06-01-今,中國電子學會雲計算專家委員會, 委員

2003-08-01-今,中國人工智能學會, 副秘書長

2003-06-01-今,中國人工智能學會知識工程與分佈智能專業委員會, 秘書長

機器學習、數據挖掘、文本挖掘、基於雲計算的分佈式並行數據挖掘等人工智能領域

1.PAKDD2018國際會議最有影響論文獎, 一等獎, 其他, 2018

2.吳文俊人工智能科學技術創新獎——大數據挖掘算法與雲服務, 二等獎, 省級, 2015

3.北京市科學技術獎——主體網格智能平台, 三等獎, 省級, 2006

1.何清：大數據挖掘領域的開拓者 ^[2] 

2.中國人工智能學會副秘書長何清：智能技術正向認知、推理階段推進 ^[3] 

1.提出了基於超曲面的覆蓋學習算法；

2.提出極小樣本集抽樣方法與相關理論；

3.提出了基於進化規劃的基於攝動的模糊聚類改進算法，解決了模糊聚類失真問題；

4.證明了模糊集擴展原理在範疇論意義下的合理性；

5.提出概念語義空間用於知識管理；

6.提出一種極端支持向量機分類算法 ^[2]  ；

7.提出基於粒度的多層次決策方法；

8.組織開發了國內最早的基於雲計算平台Hadoop的並行數據挖掘系統。

1. 國家自然科學基金面上項目：深度與寬度自適應的深度極端學習機模型研究, No.61573335， 2016年01月至 2019年 12月，負責人

2. 國家自然科學基金一年期滾動項目NO.91846113，項目名稱：一年期滾動項目——證券管理決策大數據挖掘雲服務平台研究，2019.1.1-2019.1.231

3. 國家自然科學基金大數據重大計劃培育項目：“證券管理決策大數據挖掘雲服務平台研究” No. 91546122，2016年1 月至2018年12月，負責人，圓滿完成，被評為優。。

4. 國家自然科學基金面上項目“領域適應性問題相關學習算法與理論研究”，No. 61175052,2012.1-2015.12,負責人，圓滿完成，順利結題。

5. 國家自然科學基金重點項目“WEB 搜索與挖掘的新理論與方法”，No. 60933004,2010.1-2013.12, 合作方負責人, 結題被評為優。

6. 國家自然科學基金面上項目：分佈式計算環境下的並行數據挖掘算法與理論研究，2010.1～2012.12，負責人，圓滿完成，順利結題。

7. 國家自然科學基金面上項目“基於超曲面的覆蓋分類算法與理論研究” No. 60675010，2007.1-2009.12 負責人，被評為優

8. 國家自然科學基金“概念語義空間及其應用”No.60173017，負責人：何清，2001.1-2002.12，被評為優

9. 國家“八六三”高技術研究發展計劃項目“開放環境下海量web數據提取、集成、分析和管理系統平台與應用”所屬課題“海量web數據內容管理、分析挖掘技 術與大型示範應用” No.2012AA011003, 2012.1-2014.12。子課題負責人，結題獲得好評。

10. 國家“八六三”高技術研究發展計劃“基於感知機理的智能信息處理技術”No：2006AA01Z128, 負責人，2006.9-2008.12，結題獲得好評。

11. 國家“八六三”高技術研究發展計劃“自主計算的理論和技術研究”No：2003AA115220, 負責人， 2003.7-2005.10，結題獲得好評。

12. 973項目課題“非結構化信息(圖像)的內容理解與語義表徵”No. 2007CB311004，2007.7-2012.7，骨幹，項目結題被評為優。

[1] Feiyang Pan, Jia He, Dandan Tu, QingHe. Trust the Model When It Is Confident Masked Model-based Actor-Critic, 34thConference on Neural Information Processing Systems (NeurIPS 2020), Vancouver,Canada

[2] Feiyang Pan, Xiang Ao, PingzhongTang, Min Lu, Dapeng Liu, Lei Xiao and Qing He. Field-aware calibration: asimple and empirically strong method for reliable probabilistic predictions.WWW’20, April 20–24, 2020, Taipei, China,

[3] Dongbo Xi, Fuzhen Zhuang, Bowen Song,Yongchun Zhu, Shuai Chen, Dan Hong, Tao Chen, Xi Gu, Qing He. NeuralHierarchical Factorization Machines for User's Event Sequence Analysis.SIGIR20, July 25-30, 2020, Xi'an, China.

[4] Dongbo Xi, Fuzhen Zhuang, GanbinZhou, Xiaohu Cheng, Fen Lin, Qing He. Domain Adaptation with Category AttentionNetwork for Deep Sentiment Analysis. WWW’20, April 20–24, 2020, Taipei, China, pp.:3133-3139.

[5] Yongchun Zhu, Dongbo Xi, Bowen Song,Fuzhen Zhuang, Shuai Chen, Gu Xi, and Qing He. Modeling Users’ BehaviorSequences with Hierarchical Explainable Network for Cross-domain FraudDetection. Proceedings of the Web Conference 2020. pp.: 928-938. WWW’20, April20–24, 2020, Taipei, China

[6] Qiwei Zhong，YangLiu，Xiang Ao，Binbin Hu，Jinghua Feng，Jiayu Tang，QingHe. Financial Defaulter Detection on Online Credit Payment via Multi-viewAttributed Heterogeneous Information Network，WWW’20, April 20–24, 2020, Taipei,China

[7] Zhao Zhang, Fuzhen Zhuang*, HengshuZhu, Zhiping Shi, Hui Xiong, Qing He, Relational Graph Neural Network withHierarchical Attention for Knowledge Graph Completion. AAAI 2020,Feb.7-12,Newyork USA.

[8] Dongbo Xi, Fuzhen Zhuang*, YanchiLiu, Jingjing Gu, Hui Xiong, Qing He: Modelling of Bi-directionalSpatio-Temporal Dependence and Users' Dynamic Preferences for Missing POICheck-in Identification. AAAI 2019.

[9] Feiyang Pan, Qingpeng Cai, An-XiangZeng, Chun-Xiang Pan, Qing Da, Hualin He, Qing He, Pingzhong Tang. PolicyOptimization with Model-based Explorations. AAAI 2019.

[10] Feiyang Pan, Shuokai Li, Xiang Ao, Pingzhong Tang, Qing He. Warm UpCold-start Advertisements : Improving CTR Predictions via Learning to Learn IDEmbeddings. To appear in the 42nd International ACM SIGIR Conference onResearch and Development in Information Retrieval (SIGIR 2019).

[11] Pan feiyang, Cai, Qi,Tang, Pingzhong, Zhuang, Fuzhen., He, Qing.Policy gradients for contextual recommendations,WWW2019

[12] Ying Sun, Fuzhen Zhuang, Hengshu Zhu, Xin Song, Qing He, Hui Xiong.A Structure-Aware Convolutional Neural Network Approach, KDD2019

[13] Ling Luo, Xiang Ao,Yan Song,Jinyao Li,Xiaopeng Yang,Qing He,Dong Yu,Unsupervised Neural Aspect Extraction with Sememes,IJCAI2019

[14] Ying Sun, Hengshu Zhu, Fuzhen Zhuang, Jingjing Gu and Qing HeExploring the Urban Region-of-Interest through the Analysis of Online MapSearch Queries，KDD2018

[15] Ganbin Zhou, Ping Luo, Rongyu Cao, Yijun Xiao, Fen Lin, Bo Chen,Qing He. Tree-Structured Neural Machine for Linguistics-Aware SentenceGeneration，The Thirty-Second AAAI Conference on Artificial Intelligence(AAAI-18) ，February 2–7, 2018，New Orleans, Lousiana, USA

[16] Ganbin Zhou, Ping Luo, Yijun Xiao, Fen Lin, Bo Chen, Qing He.Elastic Responding Machine for Dialog Generation with Mechanism DynamicallySelecting，The Thirty-Second AAAI Conference on Artificial Intelligence(AAAI-18) ，February 2–7, 2018，New Orleans, Lousiana, USA

[17] Jingwu Chen, Fuzhen Zhuang, Xin Hong, Xiang Ao, Xing Xie and QingHe: Attention-driven Factor Model for Explainable Personalized Recommendation.SIGIR 2018

[18] Xiang Ao, Yang Liu, Zhen Huang, Luo Zuo, Qing He. Free-rider EpisodeScreening via Dual Partition Model. The 23rd International Conference onDatabase Systems for Advanced Applications (DASFAA), 2018.

[19] Ling Luo, Xiang Ao, Feiyang Pan, Tong Zhao, Ningzi Yu, Qing He.Beyond Polarity: Interpretable Financial Sentiment Analysis with HierarchicalQuery-driven Attention. The 27th International Joint Conference on ArtificialIntelligence (IJCAI), 2018.

[20] Jia He, Changying Du, Changde Du, Fuzhen Zhuang, Qing He, GuopingLong.Nonlinear Maximum Margin Multi-view Learning with Adaptive Kernel,IJCAI17

[21] Ganbin Zhou, Ping Luo, Rongyu Cao, Fen Lin, Bo Chen, Qing He.Mechanism-Aware Neural Machine for Dialogue Response Generation，AAAI2017

[22] Xiang Ao, Ping Luo, Jin Wang, Fuzhen Zhuang, Qing He. MiningPrecise-positioning Episode Rules from Event Sequences，ICDE2017

[23] Fuzhen Zhuang, Jing Zheng, Chuan Shi and Qing He.TransferCollaborative Filtering from Multiple Sources via ConsensusRegularization,WSDM2017

[24] Qing He, Yunlong Ma, Qun Wang, Fuzheng Zhuang, Zhongzhi Shi.Parallel Outlier Detection Using KD-Tree Based on MapReduce, IEEE CloudCom 2011,Washington,DC, USA, 4-9 July, 2011

[25] Qing He, Zhongzhi Shi, Lian Ren.The Classification Method Based onHyper Surface，2002 International Joint Conference on Neural Networks，2002.5:1499-1503,Honolulu, Hawaii,USA, May 12-17, 2002

[26] Qing He, Xiurong Zhao, Sulan Zhang. Multi-modal services for webinformation collection based on multi-agent techniques, Lecture Notes inComputer Science, v 4088 LNAI, Agent Computing and Multi-Agent Systems: 9thPacific Rim International Workshop on Multi-Agents, PRIMA 2006, p 129-137,Guilin, China, in August 2006

[27] Jia He, Changying Du, Fuzhen Zhuang,Yin Xin, Qing He, Guoping Long.Online Bayesian Max-margin Subspace Multi-view Learning, IJCAI-16，July9–15, 2016, New York

[28] Ping Luo, Ganbin Zhou, Qing He*. Browsing Regularities in HedonicContent Systems: the More the Merrier? IJCAI-16，July 9–15, 2016, New York

[29] Xiang Ao, Ping Luo, Chengkai Li, Fuzhen Zhuang, Qing He. OnlineFrequent Episode Mining, ICDE 2015 : International Conference on DataEngineering (ICDE15), Seoul, Korea, April 13-17, 2015

[30] Changying Du, Shandian Zhe, Fuzhen Zhuang, Alan Qi, Qing He,Zhongzhi Shi. Bayesian Maximum Margin Principal Component Analysis,Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI-15)，Austin,Texas, USA, January 25–30, 2015,

[31] Xinyu Wu, Ping Luo, Qing He, Tianshu Feng. Festival, Date and LimitLine: Predicting Vehicle Accident Rate in Beijing, SDM15, British Columbia,Canada, April 30-May 2

[32] Xiang Ao, Ping Luo, Chengkai Li, Fuzhen Zhuang, Qing He, ZhongzhiShi.Discovering and learning sensational episodes of news events. The 23rdinternational conference on World Wide Web, WWW2014, Seoul, Korea, April 7-11,

[33] Wenjuan Luo, Fuzhen Zhuang, Xiaohu Cheng, Qing He, Zhongzhi Shi.Ratable Aspects over Sentiments: Predicting Ratings for Unrated Reviews, IEEEInternational Conference on Data Mining (ICDM 2014), Shenzhen, China,December14-17, 2014

[34] Xin Jin, Fuzhen Zhuang, Hui Xiong, Changying Du, Ping Luo and QingHe. Multi-task Multi-view Learning for Heterogeneous Tasks, CIKM’14, November03–07, 2014, Shanghai, China

[35] Fuzhen Zhuang, Xiaohu Cheng, Sinno Jialin Pan, Wenchao Yu, Qing He,Zhongzhi Shi. Transfer Learning with Multiple Sources via Consensus RegularizedAutoencoders, The European Conference on Machine Learning and Principles andPractice of Knowledge Discovery in Databases (ECML14/PKDD14), Nancy, France,September 15th to 19th, 2014.

[36] Changying Du, Jia He, Fuzhen Zhuang, Yuan Qi, Qing He. NonparametricBayesian Multi-Task Large-margin Classification, 21st European Conference onArtificial intelligence (ECAI14), Prague, Czech, 18-22 Aug. 2014.

[37] Shuo Han, Fuzhen Zhuang, Qing He, Zhongzhi Shi. Balanced SeedSelection for Budgeted Influence Maximization in Social Networks, PAKDD 2014:Pacific-Asia Conference on Knowledge Discovery and Data Mining , 2014-05-13,Tainan, Taiwan, China

[38] Xiang Ao, Ping Luo, Chengkai Li, Fuzhen Zhuang, Qing He, ZhongzhiShi. Discovering and learning sensational episodes of news events. The 23rdinternational conference on World Wide Web, WWW2014, Seoul, Korea,April 7-11

[39] Wenjuan Luo, Fuzhen Zhuang, Xiaohu Cheng, Qing He, Zhongzhi Shi.Ratable Aspects over Sentiments: Predicting Ratings for Unrated Reviews, IEEEInternational Conference on Data Mining (ICDM 2014), Shenzhen, China / December14-17, 2014

[40] Xin Jin, Fuzhen Zhuang, Hui Xiong, Changying Du, Ping Luo and QingHe. Multi-task Multi-view Learning for Heterogeneous Tasks, CIKM’14, Shanghai,China, November 03–07, 2014

[41] Shuo Han, Fuzhen Zhuang, Qing He, Zhongzhi Shi. Balanced SeedSelection for Budgeted Influence Maximization in Social Networks, PAKDD 2014 :Pacific-Asia Conference on Knowledge Discovery and Data Mining, Tainan, Taiwan,China,2014-05-13

[42] Fuzhen Zhuang, Ping Luo, Changying Du, Qing He, Zhongzhi Shi.Triplex Transfer Learning: Exploiting both Shared and Distinct Concepts forText Classification, WSDM’13, Rome, Italy, February 4–8, 2013

[43] Fuzhen Zhuang, Ping Luo, Peifeng Yin, Qing He, Zhongzhi Shi. ConceptLearning for Cross-domain Text Classification: a General ProbabilisticFramework, 23rd International Joint Conference on Artificial Intelligence(IJCAI 2013). Beijing, China, August 3-9, 2013

[44] Tianfeng Shang, Qing He, Fuzhen Zhuang and Zhongzhi Shi. A NewSimilarity Measure Based on Preference Sequence for Collaborative Filtering.Web Technologies and Applications. 15th Asia-Pacific Web Conference, APWeb2013,Sydney, NSW, Australia, 4-6 April 2013

[45] Tianfeng Shang, Qing He, Fuzhen Zhuang, Zhongzhi Shi. ExtremeLearning Machine Combining Matrix Factorization for Collaborative Filtering.IEEE The 2013 International Joint Conference on Neural Networks, IJCNN 2013,Dallas, TX, USA, August 4-9, 2013.

[46] Xin Jin, Fuzhen Zhuang, Shuhui Wang, Qing He, and Zhongzhi Shi.Shared Structure Learning for Multiple Tasks with Multiple Views, ECML/PKDD13,Prague, September 23-27, 2013

[47] Wenchao Yu, Guangxiang Zeng, Ping Luo, Fuzhen Zhuang,Qing He, andZhongzhi Shi. Embedding with Autoencoder Regularization, ECML/PKDD13,Prague,September 23-27, 2013

[48] Changying Du, Fuzhen Zhuang, Qing He and Zhongzhi Shi. Multi-TaskSemi-Supervised Semantic Feature Learning for Classification, ICDM2012，pp.191-200, Brussels, Belgium, 2012 (12/10-12/13)

[49] Wenjuan Luo Fuzhen Zhuang, Qing He, and Zhongzhi Shi. Quad-tuplePLSA: Incorporating Entity and Its Rating in Aspect Identification, The 16thPacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), PAKDD2012, pp. 392–404, Kuala Lumpur, Malaysia, 29 May - 1 June,2012

[50] Xudong Ma, Ping Luo, FuzhenZhuang, Qing He, Zhongzhi Shi andZhiyongShen. Combining Supervised and Unsupervised Models via UnconstrainedProbabilistic Embedding, Twenty-Second International Joint Conference onArtificial Intelligence, IJCAI 11，pp.1396-1401C,Barcelona in July 2011

[51] Fuzhen Zhuang, Ping Luo, Hui Xiong, Qing He. Yuhong Xiong.Exploiting Associations between Word Clusters and Document Classes forCross-domain Text Categorization, 2010 SIAM International Conference on DataMining (SDM'2010), pp.13-24, Columbus, Ohio, April 19, 2010(最佳論文提名)

[52] Fuzhen Zhuang, Ping Luo, Zhiyong Shen, Qing He, Yuhong Xiong, andZhongzhi Shi. D-LDA: A Topic Modeling Approach without Constraint Generationfor Semi-Defined Classification, accepted as a regular paper at the IEEEInternational Conference on Data Mining (ICDM 2010) to be held in SydneyAustralia, December 14-17，2010, pp.709-718, (EI )

[53] Fuzhen Zhuang, Ping Luo, Zhiyong Shen, Qing He, Yuhong Xiong,Zhongzhi Shi1, Hui Xiong. Collaborative Dual-PLSA: Mining Distinction andCommonality across Multiple Domains for Classification, The 19th ACMInternational Conference on Information and Knowledge Management( CIKM’10),October 26-30, 2010, Toronto, Canada. (最佳論文提名)

[54] Qing Tan, Qing He, Zhongzhi Shi. Nonparametric Curve ExtractionBased on Ant Colony System, Proceedings of the Twenty-Fourth AAAI Conference onArtificial Intelligence (AAAI-10), pp.599-604, Atlanta, USA, July 10-15, 2010

[55] Weizhong Zhao, Huifang Ma, Qing He.Parallel k-means clustering basedon mapreduce, Cloud Computing, 2009

[56] Ping Luo, Fuzhen Zhuang, Hui Xiong, Yuhong Xiong, Qing He. TransferLearning from Multiple Source Domains via Consensus Regularization, full paperin CIKM 2008 , Napa Valley, California October 26-30, 2008

[57] Qiuge Liu, Qing He, Zhongzhi Shi. Extreme Support Vector MachineClassify, Lecture Notes in Computer Science, v 5012 LNAI, Advances in KnowledgeDiscovery and Data Mining, 12th Pacific-Asia Conference, PAKDD 2008,Proceedings, 2008, p 222-233,Osaka,Japan,May 20-23,2008(2018年被評為最有影響的論文)

[58] Luo, Ping; Lu, Kevin; He, Qing; Shi, Zhongzhi. A heterogeneouscomputing system for data mining workflows, Lecture Notes in Computer Science,v 4042 LNCS, Flexible and Efficient Information Handling - 23rd BritishNational Conference on Databases, BNCOD 23, Proceedings, 2006, p 177-189,Belfast, Northern Ireland, UK, July 18-20, 2006

[59] Zheng, Zheng; He, Qing; Shi, Zhongzhi. Granule sets based bileveldecision model, Lecture Notes in Computer Science, v 4062, Rough Sets andKnowledge Technology - First International Conference, RSKT 2006, Proceedings,2006, p 530-537, Chongqing, China, July 24-26, 2006

[60] Zhao, Xiu-Rong; He, Qing; Shi, Zhong-Zhi. HyperSurface Classifiersensemble for high dimensional data sets, Lecture Notes in Computer Science, v3971, Advances in Neural Networks - ISNN 2006: Third International Symposium onNeural Networks, p 1299-1304, Chengdu, China, May 28 - June 1, 2006

[61] Ping Luo, Qing He, Rui Huang, Fen Lin, Zhongzhi Shi. ExecutionEngine of Meta-learning System for KDD in Multi-agent Environment. LectureNotes in Computer Science. Springer-Verlag, Volume 3505 / 2005, 149-160.AIS-ADM 2005, St. Petersburg, Russia, June 6-8, 2005

[62] Ping Luo, Su Yan, Zhiqiang Liu, Zhiyong Shen, Shengwen Yang, QingHe. From Online Behaviors to Offline Retailing, the ACM KDD 2016 Conference asa full presentation.

[1] Yang Liu, Xiang Ao, Linfeng Dong,Chao Zhang, Jin Wang, Qing He. Spatiotemporal Activity Modeling viaHierarchical Cross-Modal Embedding. Accepted by IEEE Transactions on Knowledgeand Data Engineering. 2020.

[2] Jingwu Chen, Fuzhen Zhuang, TianxinWang, Leyu Lin, Feng Xia, Lihuan Du, Qing He. Follow the Title then Read theArticle: Click-guide Network for Dwell Time Prediction, Accepted by IEEETransactions on Knowledge and Data Engineering

[3] Fuzhen Zhuang, Yingmin Zhou, HaochaoYing, Fuzheng Zhang, Xiang Ao, Xing Xie, Qing He, Hui Xiong: SequentialRecommendation via Cross-domain Novelty Seeking Trait Mining. Accepted byJournal of Computer Science and Technology, 2020.

[4] Yongchun Zhu, Fuzhen Zhuang, JindongWang, Guolin Ke, Jingwu Chen, Jiang Bian, Hui Xiong, Qing He: Deep Subdomain AdaptationNetwork for Image Classification. Accepted by IEEE Transactions on NeuralNetworks and Learning Systems.

[5] Jia He, Changying Du,Fuzhen Zhuang,Xin Yin, Qing He. Guoping Long. Online Bayesian Max-margin Subspace Learningfor Multi-view Classification and Regression, Machine Learning, (2020)109:219–249.

[6] Zhao Zhang, Fuzhen Zhuang, XuebingLi, Zhengyu Niu, Jia He, Qing He, Hui Xiong: Knowledge Triple Mining viaMulti-Task Learning. Information Systems, Information Systems 80 (2019) 64–75

[7] Xiang Ao, Haoran Shi, Jin Wang, LuoZuo, Hongwei Li, Qing He:Large-Scale Frequent Episode Mining from Complex EventSequences with Hierarchies. ACM TIST 10(4): 36:1-36:26 (2019)

[8] Thapana Boonchooa, Xiang Ao, YangLiu, Weizhong Zhao, Fuzhen Zhuang, Qing He. Grid-based DBSCAN : Indexing andInference. Pattern Recognition (PR), 90 : 271-284, 2019

[9] Zhou Ganbin Luo Ping He Qing.Predicting Compositional Time Series via Autoregressive DirichletEstimation[J]. Science China Information Sciences, 2018, 61(9):098-106.

[10] Xiang Ao, Ping Luo, Jin Wang, Fuzhen Zhuang and Qing He. MiningPrecise-positioning Episode Rules from Event Sequences, IEEE Transactions onKnowledge & Data Engineering, vol. 30, no. 3, pp. 530-543, 2018.

[11] Fuzhen Zhuang, Xuebing Li, Xin Jin, Dapeng Zhang, Lirong Qiu, QingHe. Semantic Feature Learning for Heterogeneous Multitask Classification viaNon-Negative Matrix Factorization. IEEE Trans. Cybernetics 48(8): 2284-2293(2018)

[12] Jie Lu, Zheng Zheng, Guangquan Zhang, Qing He and Zhongzhi Shi. Anew solution algorithm for solving rule-sets based bilevel decision problems,CONCURRENCY AND COMPUTATION: PRACTICE AND EXPERIENCE. Vol: 27, No: 4,pages:830-54

[13] Wenjuan Luo, Fuzhen Zhuang, Weizhong Zhao, Qing He, Zhongzhi Shi.QPLSA: Utilizing quad-tuples for aspect identification and rating, InformationProcessing and Management 51 (2015) 25–41

[14] Wenchao Yu, Fuzhen Zhuang, Qing He and Zhongzhi Shi. Learning DeepRepresentations via Extreme Learning Machine, Neurocomputing, Volume 149, PartA, 3 February 2015, Pages 308-315

[15] Xiang Ao; Ping Luo; Xudong Ma; Fuzhen Zhuang; Qing He; Zhongzhi Shi;Zhiyong Shen. Combining Supervised and Unsupervised Models via UnconstrainedProbabilistic Embedding, Information Sciences, 257 (2014) 101–114.

[16] Fuzhen Zhuang, Ping Luo, Changying Du, Qing He, Zhongzhi Shi, HuiXiong: Triplex transfer learning: exploiting both shared and distinct conceptsfor text classification, IEEE TRANSACTIONS ON CYBERNETICS, VOL. 44, NO. 7, 1191-1203,JULY 2014

[17] Shuo Han, Fuzhen Zhuang, Qing He, Zhongzhi Shi, & Xiang Ao.Energy model for rumor propagation on social networks. Physica A: StatisticalMechanics and its Applications，394 (2014) 99–109.

[18] Shuo Han，Qing He，Zhongzhi Shi. Energy Model for Rumor Propagation on Social Networks.Physica A: Statistical Mechanics and its Applications，394(2014) 99–109.

[19] Wenjuan Luo, Fuzhen Zhuang, Qing He, Zhongzhi Shi Exploitingrelevance, coverage, and novelty for query-focused multi-document summarization，Knowledge-BasedSystems. Volume 46, July 2013, Pages 33–42.

[20] Fuzhen Zhuang, Ping Luo, Zhiyong Shen, Qing He, Yuhong Xiong,Zhongzhi Shi and Hui Xiong. Mining Distinction and Commonality across MultipleDomains using Generative Model for Text Classification, IEEE Transactions onKnowledge and Data Engineering, VOL. 24, NO. 11, NOVEMBER 2012,2025-2039(SCI\EI)

[21] Zhiping Shi, Xi Liu, Qingyong Li, Qing He, Zhongzhi Shi, ExtractingDiscriminative Features for CBIR, MULTIMEDIA TOOLS AND APPLICATIONS，Volume 61, Number 2 (2012), 263-279(SCI)

[22] Fuzhen Zhuang, George Karypis, Xia Ning, Qing He, Zhongzhi Shi.Multi-view learning via probabilistic latent semantic analysis, InformationSciences,199 (2012) 20–30

[23] Weizhong Zhao, Qing He, Huifang Ma, Zhongzhi Shi. EffectiveSemi-supervised Document Clustering via Active Learning with Instance-levelConstraints, Knowledge and Information Systems (2012) 30:569–587

[24] Tan, Qing; He, Qing; Zhao, Weizhong; Shi, Zhongzhi; Lee, E.S. Animproved FCMBP fuzzy clustering method based on evolutionary programming,Computers and Mathematics with Applications, v 61, n 4, p 1129-1144, February2011

[25] Guang-Quan Zhang, ZhengZheng, Jie Lu, Qing He*. An Algorithm forSolving Rule-Sets Based Bilevel Decision Problems, COMPUTATIONAL INTELLIGENCEVol.27 No.2 pp.235-259, 2011

[26] Fuzhen Zhuang, Ping Luo, Hui Xiong, Yuhong Xiong, Qing He*, andZhongzhi Shi. Cross-Domain Learning from Multiple Sources: A ConsensusRegularization Perspective, IEEE TRANSACTIONS ON KNOWLEDGE AND DATAENGINEERING, December 2010 (vol. 22 no. 12) ,pp. 1664-1678

[27] Zheng, Z., Lu, J, Zhang G, He Q*, Rule sets based bilevel decisionmodel and algorithm, Expert Systems with Applications, 2009. Vol. 36, No. 1,18-26

[28] Shifei Ding, Yongping Zhang, Xiaofeng Lei, Xinzheng Xu, Xin Wang, LiWang, Qing He*. Research on a principal components decision algorithm based oninformation entropy, Journal of Information Science, Vol. 35, No. 1, 120-127(2009)

[29] Zhuang F Z, Luo P, He Q, et al. Inductive transfer learning forunlabeled target-domain via hybrid regularization. Chinese Sci Bull, 2009, 54:2470―2478

[30] Zhiping Shi, Qing He*, Zhongzhi Shi. An Index and RetrievalFramework Integrating Perceptive Features and Semantics for MultimediaDatabase. Multimedia Tools and Application (2009) 42:207–231 Springer

[31] Zheng, Z., Lu, J, Zhang G, He Q, Rule sets based bilevel decisionmodel and algorithm, Expert Systems with Applications, 2009. Vol. 36, No. 1,18-26

[32] Ping Luo, Guoxing Zhan, Qing He, Zhongzhi Shi, and Kevin Lu, OnDefining Partition Entropy by Inequalities. IEEE TRANSACTIONS ON INFORMATIONTHEORY, v53, n 9, SEPTEMBER 2007, p 3233-3239.

[33] Ping Luo; Lu, Kevin; Shi, Zhongzhi; He, Qing. Distributed datamining in grid computing environments. Future Generation Computer Systems, v23, n 1, Jan 1, 2007, p 84-91

[34] Zhongzhi Shi; Huang, Youping; He, Qing; Xu, Lida; Liu, Shaohui; Qin,Liangxi; Jia, Ziyan; Li, Jiayou; Huang, Huijing; Zhao, Lei. MSMiner-adeveloping platform for OLAP. Decision Support Systems, v 42, n 4, January,2007, Decision Support Systems in Emerging Economies, pp. 2016-2028

[35] Luo, Ping; Lu, Kevin; Shi, Zhongzhi; He, Qing. Distributed datamining in grid computing environments, Future Generation Computer Systems, v23, n 1, Jan 1, 2007, p 84-91

[36] Luo, Ping; Lu, Kevin; Huang, Rui; He, Qing; Shi, Zhongzhi. Aheterogeneous computing system for data mining workflows in multi-agentenvironments, Expert Systems, v 23, n 5, November, 2006, p 258-271

[37] Shi, Zhongzhi; Huang, Youping; He, Qing; Xu, Lida; Liu, Shaohui;Qin, Liangxi; Jia, Ziyan; Li, Jiayou; Huang, Huijing; Zhao, Lei. MSMiner-adeveloping platform for OLAP, Decision Support Systems v 42,n 4,2007 p2016-2028

[38] Qing He, Haocheng Wang, Fuzhen Zhuang, Tianfeng Shang, Zhongzhi Shi.Parallel sampling from big data with uncertainty distribution, Fuzzy Sets andSystems 258 (2015) 117–133

[39] Qing He, Xin Jin, Changying Du, Fuzhen Zhuang and Zhongzhi Shi.Clustering in extreme learning machine feature space. Neurocomputing 128 :88-95 (2014)..

[40] Qing He, Tianfeng Shang, Fuzhen Zhuang and Zhongzhi Shi. Parallel ExtremeLearning Machine for Regression based on MapReduce, Neurocomputing102(2013)52–58

[41] He, Qing; Zhao, Weizhong; Shi, Zhongzhi. CHSMST: A clusteringalgorithm based on hyper surface and minimum spanning tree, Soft Computing, v15, n 6, p 1097-1103, June 2011

[42] Qing He, Changying Du, Qun Wang, FuzhenZhuang, Zhongzhi Shi. AParallel Incremental Extreme SVM Classifier, Neurocomputing，74(2011) 2532–2540

[43] Qing He, Xiurong Zhao, Zhongzhi Shi.Minimal consistent subset forHyper Surface Classification method. INTERNATIONAL JOURNAL OF PATTERNRECOGNITION AND ARTIFICIAL INTELLIGENCE Volume: 22 Issue: 1 Pages: 95-108, FEB2008.

[44] Qing He, Xiurong Zhao, Zhongzhi Shi. Classification based ondimension transposition for high dimension data，International Journal Soft Computing11(4),2007, pp: 329 - 334

[45] Qing He, Zhongzhi Shi,Li-an Ren, E.S. Lee. A Novel ClassificationBased on Hypersurface. International Journal of Mathematical and ComputerModeling 38(2003),395-407

[46] Qing He, Hongxing Li, Zhongzhi Shi, E.S.Lee. On Fuzzy ClusteringMethod Based on Perturbation. Computers and Mathematics with Applications, v46, n 5-6, September, 2003, p 929-946

[47] Qing He, Hongxing Li, C.L.P. Chen, E.S. Lee. Extension Principlesand Fuzzy Set Categories. International Journal of Computers and Mathematicswith Applications 2000, 39: 45-53

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