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丁世飞

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丁世飞 ，男，汉族，1963年出生 ，山东省青岛市人 ，博士研究生 ，工学博士，中国科学院计算所博士后 ，中国矿业大学教授、博士生导师。

基本信息

人物说明----中国矿业大学教授、博士生导师

民 族 ---- 汉族

出生地点----山东省青岛市

出生日期----1963年

国 籍 ---- 中国

职 业 ---- 教育科研工作者

主要成就----全国智能信息处理学术会议(NCIIP)程序委员会主席

毕业院校----中国科学院计算技术研究所

代表作品----孪生支持向量机:算法与拓展

单 位 ---- 中国矿业大学

职 称 ---- 教授

学 位 ---- 中国科学院计算所博士后

人物简历

博士研究生学历，工学博士学位。

中国科学院计算技术研究所博士后。

1963年，生于山东省青岛市。　　

2001-2004，山东科技大学攻读博士学位，导师为靳奉祥教授。

2004-2006，中国科学院计算技术研究所计算机科学与技术博士后流动站从事博士后研究，导师为史忠植教授。

现任:

中国科学院计算技术研究所客座研究员、博士生导师

中国矿业大学教授、博士生导师

中国矿业大学计算机应用技术博士点学科带头人

中国矿业大学计算机科学与技术学院教授委员会主任

中国矿业大学-中国科学院智能信息处理联合实验室负责人

学术兼职

担任下列专家委员会委员:

(1)中国计算机学会杰出会员、资深会员

(2)中国计算机学会理事

(3)中国人工智能学会理事

(4)中国计算机学会人工智能与模式识别专委会委员

(5)中国计算机学会多值逻辑与模糊逻辑专委会常委委员

(6)中国人工智能学会知识工程与分布式智能专业委员会委员

(7)中国人工智能学会机器学学习专业委员会委员

(8)中国人工智能学会粒计算与知识发现专业委员会常委委员

(9)江苏省计算机学会人工智能专业委员会常委委员

(10)江苏省计算机学会大数据专家委员会委员

(11)江苏省人工智能学会理事

(12)江苏省人工智能学会机器学习专委会副主任委员

担任下列国际期刊编委:

(1)《IJCI: International Journal of Collaborative Intelligence》主编

(2)《JDCTA: Journal of Digital Contents Technology and Application》副主编

(3)《JCIT: Journal of Convergence Information Technology》编委

(4)《AISS: Advances in Information Sciences and Service Sciences》编委

(5)《IJACT: International Journal of Advancements in Computing Technology》编委

(6)《JCP: Journal of Computers》编委

(7)《JSW: Journal of Software》编委

(8)《IPL:CInformation Processing Letters》编委

(9)《AMIS: Applied Mathematics & Information Sciences》编委

担任下列国际期刊特约编辑:

(1)《Applied Mathematics & Information Sciences》特约编辑(Guest Editor)

(2)《INFORMATION》的特约编辑(Guest Editor)

(3)《Neurocpmputing》特约编辑(Guest Editor)

(4)《The Scientific World Journal》的特约编辑(Guest Editor)

(5)《Mathematical Problems in Engineering》的特约编辑(Guest Editor)

(6)《Journal of Computers (JCP)》特约编辑(Guest Editor)

(7)《Journal of Software (JSW)》特约编辑(Guest Editor)

(8)《Journal of Networks (JNW)》的特约编辑(Guest Editor)

担任下列国际SCI源刊特约审稿专家:

(1)《Journal of Information Science》

(2)《Applied Soft Computing》

(3)《Information Sciences》

(4)《Computational Statistics and Data Analysis》

(5)《IEEE Transactions on Fuzzy Systems》

(6)《International Journal of Pattern Recognition and Artificial Intelligence》

(7)《Neurocpmputing》

(8)《Soft Computing》

(9)《Pattern Recognition》

(10)《Pattern Recognition Letters》

担任下列国内核心期刊审稿专家:

(1)《计算机学报》

(2)《软件学报》

(3)《计算机研究与发展》

(4)《中国科学》(中英文版)

(5)《电子学报》(中英文版)

(6)《模式识别与人工智能》

(7)《计算机科学》

(8)《小型微型计算机系统》

(9)《计算机应用研究》

(10)《计算机工程与科学》

(11)《计算机工程与应用》

(12)《计算机科学与探索》

(13)《微电子学与计算机》

担任下列国内外会议PC Chair or Member:

(1)全国智能信息处理学术会议(NCIIP)程序委员会主席

(2)江苏省人工智能学术会议程序委员会主席

(3)2012\2013\2014年信息、智能与计算国际研讨会主席

(4)粒度计算国际会议程序委员会委员

(5)智能信息处理国际会议程序委员会委员

(6)中国机器学习会议程序委员会委员

(7)中国粗糙集与软计算、中国粒计算、中国Web智能联合会议程序委员会委员等

担任下列项目评审专家:

(1)国家863、973项目评审专家

(2)国家自然科学奖、国家科技进步奖、国家科技发明奖(三大奖)评审专家

(3)国家自然科学基金重点项目、面上项目、青年项目评审专家

研究方向

模式识别与人工智能

机器学习与数据挖掘

粒度计算与知识发现

感知与认知计算

大数据智能分析

神经网络与深度学习

支持向量机与孪生支持向量机

科研项目

已经完成的项目

1. 2001-2003参加并完成国家自然科学基金项目"信息模式识别理论及其在地学中的应用"的研究(项目编号: 40074001)

2. 1999-2001主持完成省教育厅项目"信息模式识别理论及其在害虫预测预报中的应用研究"

3. 1998-2000主持完成省教育厅项目"农作物病虫害现代生物数学预报技术研究"

4. 2005-2006主持中国博士后科学基金项目"视感知学习理论及其应用研究"(No.2005037439)

5. 2004-2006主持山东省作物生物学国家重点实验室开放基金项目"山东省玉米病虫害数字模式分类的研究"(No.20040010)

6. 2006-2008参加国家自然科学基金项目"多元数据的信息模式研究与地学数据分析"(No.40574001)

7. 2006-2009参加国家863高技术项目"基于感知机理的智能信息处理技术"(No. 2006AA01Z128)

8. 2007-2010主持中国科学院智能信息处理重点实验室开放基金项目"基于认知的模式特征分析理论与算法研究"(No.IIP2006-2)

9. 2010-2012主持江苏省基础研究计划(自然科学基金)项目"面向高维复杂数据的粒度知识发现研究"(No.BK2009093)

10.2011-2012主持北京邮电大学智能通信软件与多媒体北京市重点实验室开放课题 "粒度SVM方法与应用研究"

11. 2010-2012参加国家自然科学基金项目"分布式计算环境下的并行数据挖掘算法与理论研究"(No.60975039)

12. 2011-2013主持中国科学院智能信息处理重点实验室开放基金项目"高维复杂数据的粒度支持向量机理论与算法研究"(No.IIP2010-1)

正在进行的项目

1. 2013.1-2017.12主持国家重点基础研究发展计划(973计划)课题"脑机协同的认知计算模型"(No.2013CB329502)

2. 2014.1-2017.12主持国家自然科学基金项目"面向大规模复杂数据的多粒度知识发现关键理论与技术研究" (No. 61379101)

3. 2017.1-2020.12，主持中央高校基本业务费学科重点项目"基于大数据粒化的多粒多层神经网络及其优化方法研究"(No.2017XKZD03)

论文著作

已出版著作

1. 丁世飞,靳奉祥,赵相伟著. 现代数据分析与信息模式识别. 北京:科学出版社,2012

2.丁世飞编著. 人工智能. 北京: 清华大学出版社, 2010

3. 丁世飞编著.人工智能(第2版). 北京: 清华大学出版社, 2015

4. 丁世飞编著. 高级人工智能. 徐州:中国矿业大学出版社, 2015

5. 丁世飞著. 孪生支持向量机:理论与拓展. 北京: 科学出版社, 2017

已发表论文

2017年

国内期刊

[1]曾凯,丁世飞. 图像超分辨率重建的研究进展. 计算机工程与应用, 2017, 53(16):29-35.

[2]丁世飞,张楠,史忠植. 拉普拉斯极速学习机.软件学报,2017,28(10):2599-2610.

[3] 丁世飞,黄华娟.最小二乘孪生参数化不敏感支持向量回归机.软件学报, 2017, 28(12):3146−3155.

国际SCI期刊

[1]Jian Zhang, Shifei Ding, Yu Xue. Weight Uncertainty in Boltzmann Machine. Cognitive Computation, 2016, 8(6): 1064-1073

[2] Xiekai Zhang, Shifei Ding, Yu Xue. An improved multiple birth support vector machine for pattern classification.Neurocomputing, 2017, 225:119-128

[3] Shifei Ding, Yuexuan An, Xiekai Zhang, Yu Xue. Wavelet Twin Support Vector Machines based on Glowworm Swarm Optimization.Neurocomputing, 2017, 225:157-163

[4] Nan Zhang, Shifei Ding, Jian Zhang, Yu Xue. Research on Point-wise Gated Deep Networks,Applied Soft Computing, 2017,52:1210–1221

[5]Shifei Ding, Xiekai Zhang, Yuexuan An, Yu Xue. Weighted linear loss multiple birth support vector machine based on information granulation for multi-class classification. Pattern Recognition, 2017,67:32-46

[6]Weixin Bian,Shifei Ding,Yu Xue.Combining weighted linear project analysis with orientation diffusion for fingerprint orientation field reconstruction. Information Sciences ,2017,396:55-71

[7]Shifei Ding, Lingheng Meng, Youzhen Han, Yu Xue. A Review on Feature Binding Theory and Its Functions Observed in Perceptual Process.Cognitive Computation,2017,9(2):194-206

[8] Nan Zhang, Shifei Ding. Unsupervised and semi-supervised extreme learning machine with wavelet kernel for high dimensional data. Memetic Computing, 2017,8(2)

[9]Shifei Ding, Nan Zhang, Jian Zhang, Xinzheng Xu, Zhongzhi Shi. Unsupervised extreme learning machine with representational features.International Journal of Machine Learning and Cybernetics, 2017, 8(2):587-595

[10]Shifei Ding, Zhibin Zhu, Xiekai Zhang. An overview on semi-supervised support vector machine. Neural Computing and Applications, 2017, 28(5): 969-978

[11]Shifei Ding, Lili Guo, Yalu Hou. Extreme learning machine with kernel model based on deep learning. Neural Computing and Applications, 2017, 28(8):1975-1984

[12] Mingjing Du,Shifei Ding, Yu Xue. A novel density peaks clustering algorithm for mixed data. Pattern Recognition Letters, 2017, 97:46-53

[13] Shifei Ding, Mingjing Du, Tongfeng Sun, Xiao Xu, Yue Xue. An entropy-based density peaks clustering algorithm for mixed type data employing fuzzy neighborhood. Knowledge-Based Systems, 2017, 133:294-313

[14]Shifei Ding, Weixin Bian, Tongfeng Sun, Yu Xue. Fingerprint enhancement rooted in the spectra diffusion by the aid of the 2D adaptive Chebyshev band-pass filter with orientation-selective. Information Sciences, 2017, 415-416:233-246

[15] Lingheng Meng, Shifei Ding, Yu Xue. Research on denoising sparse autoencoder. International Journal of Machine Learning and Cybernetics, 2017, 8(5):1719-1729

[16] Weixin Bian, Shifei Ding, Yu Xue. Fingerprint image super resolution using sparse representation with ridge pattern prior by classification coupled dictionaries. IET Biometrics, 2017, 6(5):342-350

[17]Hongjie Jia, Shifei Ding, Mingjing Du. A Nyström Spectral Clustering Algorithm Based on Probability Incremental Sampling. Soft Computing,2017,21(19):5815–5827

[18]Shifei Ding, Nan Zhang, Xiekai Zhang, Fulin Wu. Twin support vector machine: theory, algorithm and applications. Neural Computing and Applications, 2017, 28(11):3119-3130

[19] Shifei Ding, Weixin Bian, Hongmei Liao, Tongfeng Sun, Yu Xue. Combining Gabor filtering and classification dictionaries learning for fingerprint enhancement.IET Biometrics, 2017,6(6):438-447

2016年

国内期刊

[1]张谢锴,丁世飞. 基于马氏距离的孪生支持向量. 计算机科学, 2016, 43(3):49-53

[2]樊淑炎, 丁世飞. 基于多尺度的改进Graph cut算法. 山东大学学报(工学版), 2016, 46 (1): 28-33

[3]孟令恒, 丁世飞. 基于单静态图像的深度感知模型研究. 山东大学学报(工学版), 2016, 46(3):37-43

[4]朱强波,丁世飞. 基于GA优化自适应NSCT-PCNN图像融合.小型微型计算机系统, 2016

37 (7): 1583-1587.

[5]马恒, 丁世飞. 一种基于混合数据的相似性度量的谱聚类算法. 小型微型计算机系统, 2016, 37 (8): 1751-1754.

[6]王婷婷, 丁世飞. 基于资格迹的RBF非线性强化学习研究. 小型微型计算机系统, 2016, 37 (7): 1508-1512

国际SCI期刊

[1]Shifei Ding, Mingjing Du, Hong Zhu. Survey on Granularity Clustering. Cognitive Neurodynamics. 2015, 9(6):561-572

[2] Nan Zhang, Shifei Ding, Zhongzhi Shi. Denoising Laplacian multi-layer extreme learning machine. Neurocomputing, 2016, 171: 1066-1074

[3] Jian Zhang, Shifei Ding, Nan Zhang, Zhongzhi Shi. An Incremental Extreme Learning Machine Based on Deep Feature Embedded. International Journal of MachineLearning and Cybernetics, 2016, 7(1):111-120

[4]Shifei Ding, Jian Zhang, Hongjie Jia, Jun Qian. An Adaptive Density Data Stream Clustering Algorithm. Cognitive Computation, 2016, 8(1):30-38

[5] Shifei Ding, Xiekai Zhang, Junzhao Yu. Twin support vector machines based on fruit fly optimization algorithm. Journal International Journal of Machine Learning and Cybernetics, 7(2):193-203

[6]Xiekai Zhang, Shifei Ding, Tongfeng Sun. Multi-class LSTMSVM based on optimal directed acyclic graph and shuffled frog leaping algorithm.International Journal of MachineLearning and Cybernetics, 2016, 7(2): 241-251

[7] Nan Zhang, Shifei Ding. Multi Layer ELM-RBF for Multi-Label Learning. Applied Soft Computing, 2016, 43:535-545

[8] Mingjing Du,Shifei Ding, Hongjie Jia. Study on Density Peaks Clustering Based on k-Nearest Neighbors and Principal Component Analysis. Knowledge-Based Systems, 2016, 99:135-145

[9] Shifei Ding, Jian Zhang, Xinzheng Xu, Yanan Zhang. A Wavelet Extreme Learning Machine. Neural Computing and Applications, 2016, 27(4):1033-1040

[10]Li Xu, Shifei Ding, Xinzheng Xu, Nan Zhang. Self-adaptive Extreme Learning Machine Optimized by Rough Set Theory and Affinity Propagation Clustering. Cognitive Computation, 8(4):720-728

[11] Hongmei Liao, Shifei Ding, Miaomiao Wang, Gang Ma. An Overview on Rough Neural networks. Neural Computing and Applications, 2016, 27(7): 1805–1816

[12] Hongjie Jia,Shifei Ding, Mingjing Du, Yu Xue. Approximate normalized cuts without Eigen-decomposition. Information Sciences, 2016, 374:135-150

[13] Shifei Ding, Zhongzhi Shi, Dacheng Tao, Bo An. Recent Advances in Support Vector Machines. Neurocomputing, 2016, 211:1-3

[14]Hui Li, Xuesong Wang, Shifei Ding. Research of multi-sided multi-granular neural network ensemble optimization method. Neurocomputing, 2016, 197:78-85

[15] Guanying Wang, Xinzheng Xu, Xiangying Jiang, Shifei Ding. Medical image registration based on self-adapting pulse-coupled neural networks and mutual information. Neural Computing and Applications, 2016, 27(7): 1917-1926

[16] Tongfeng Sun, Shifei Ding, Wei Chen, Xinzheng Xu. No-reference image quality assessment based on gradient histogram response.Computers & Electrical Engineering, 2016, 54:330-344

2015年

国内期刊

[1] 鲍丽娜,丁世飞, 许新征, 孙统风.基于邻域粗糙集的极速学习计算法. 济南大学学报.自然科学版, 2015, 29(5):367-371

[2] 花小朋,丁世飞. 基于鲁棒局部嵌入的孪生支持向量机. 中南大学学报(自然科学版), 2015, 46(1):149-156

[3] 黄华娟,丁世飞, 史忠植. 光滑CHKS孪生支持向量回归机.计算机研究与发展, 2015, 52(3): 569-578

[4] 郭丽丽, 丁世飞. 深度学习研究进展. 计算机科学, 2015, 42(5):28-33. [2015.5]

[5] 贾洪杰, 丁世飞, 史忠植. 求解大规模谱聚类的近似加权核k-means算法. 软件学报, 2015, 26(11):2836−2846

国际SCI期刊

[1] Shifei Ding, Zhongzhi Shi, Ke Chen, Ahmad T. Azar. Mathematical Modeling and Analysis of Soft Computing. Mathematical Problems in Engineering, vol.2015, Article ID 578321, 2 pages, 2015. doi:10.1155/2015/578321

[2] Shifei Ding, Han Zhao, Yanan Zhang, Xinzheng Xu, Ru Nie. Extreme Learning Machine algorithm Theory and Applications. Artificial Intelligence Review, 2015, 44(1): 103-115

[3]Shifei Ding,Nan Zhang,Xinzheng Xu, Lili Guo,and Jian Zhang. Deep Extreme Learning Machine and Its Application in EEG Classification. Mathematical Problems in Engineering

Volume 2015 (2015), Article ID 129021, 11 pages (http://dx.doi.org/10.1155/2015/129021)

[4] Hongjie Jia, Shifei Ding, Mingjing Du. Self-Tuning p-Spectral Clustering Based on Shared Nearest Neighbors. Cognitive Computation, 2015,7(5):622-632

[5] Shifei Ding, Mingjing Du, Hong Zhu. Survey on Granularity Clustering. Cognitive Neurodynamics. 2015, 9(6):561-572

[6] Wang Guanying, Ding Shifei, Jiang, Xiangying, Zhao, Zuopeng. A new method for constructing granular neural networks based on rule extraction and extreme learning machine. Pattern Recognition Letters, 2015,67:138-144

[7] Weikuan JIa, Dean Zhao, Tian Shen, Shifei Ding, Yuyan Zhao, Chanli Hu. An optimized classification algorithm by BP neural network based on PLS and HCA. Applied Intelligence, 2015, 43(1):176-191

[8] Liao Hongmei, Ding Shifei. Mixed and Continuous strategy Monitor-Forward Game based Selective Forwarding Solution in WSN.International Journal of Distributed Sensor Networks, 2015 (10.1155/2015/359780)

[9]Shifei Ding, Hui Li. Twice clustering based individual neural network generation method. Neurocomputing, 2015, 157, 264-272

[10]Xiaopeng Hua, Shifei Ding. Weighted least squares projection twin support vector machines with local information. Neurocomputing, 2015, 160:228-237

[11]Shifei Ding, Huanjuan Huang, Junzhao Yu, Han Zhao. Research on the hybrid models of granular computing and support vector machine.Artificial Intelligence Review, 2015,43(4):565-577

[12]Shifei Ding, Fulin Wu, Jun Qian, Hongjie Jia, Fengxiang Jin. Research on data stream clustering algorithm. Artificial Intelligence Review, 2015, 43(4): 593-600

2014年

[1] Shifei Ding, Hongjie Jia, Liwen Zhang, Fengxiang Jin. Research of semi-supervised spectral clustering algorithm based on pairwise constraints. Neural Computing and Applications, 2014,24(1):211-219. (SCI, EI)

[2] Shifei Ding, Hongjie Jia, Jinrong Chen, Fengxiang Jin. Granular Neural Networks.Artificial Intelligence Review, 2014,41(3): 373-384. (SCI, EI)

[3] Shifei Ding, Huajuan Huang, Xinzheng Xu, Jian Wang. Polynomial Smooth Twin Support Vector Machines. Applied Mathematics & Information Sciences, 2014, 8(4) (SCI,EI)

[4] Shifei Ding, Zhongzhi Shi. Track on Intelligent Computing and Applications. Neurocomputing, 2014, vol.130, 1-2.(SCI, EI)

[5] Shifei Ding, Xiaopeng Hua. Recursive least squares projection twin support vector machines. Neurocomputing, 2014, vol.130, 3-9. (SCI, EI)

[6]花小朋,丁世飞. 局部保持对支持向量机. 计算机研究与发展, 2014, 51(3)(EI)

2013年

[1] Xinzheng Xu, Shifei Ding, Weikuan Jia, Gang Ma, Fengxiang Jin. Research of assembling optimized classification algorithm by neural network based on Ordinary Least Squares (OLS). Neural Computing and Applications, 2013,22(1):187-193.(SCI, EI)

[2] Shifei Ding, Hui Li, Chunyang Su, Junzhao Yu, Fengxiang Jin. Evolutionary artificial neural networks: a review. Artificial Intelligence Review, 2013, 39(3):251-260. (SCI, EI)

[3] Li Hui, Ding Shifei. Research of Individual Neural Network Generation and Ensemble Algorithm Based on Quotient Space Granularity Clustering. Applied Mathematics & Information Sciences, 2013, 7(2):701-708. (SCI, EI)

[4] Hui Li, Shifei Ding. Research and Development of Granular Neural Networks. Applied Mathematics & Information Sciences, 2013, 7(3):1251-1261.(SCI, EI)

[5] Shifei Ding, Bingjuan Qi, Hongjie Jia, Hong Zhu. Research of Semi-supervised Spectral Clustering Based on Constraints Expansion. Neural Computing and Applications, 2013, 22 (Suppl 1):405-410. (SCI, EI)

[6] Shifei Ding, Yanan Zhang, Jinrong Chen, Weikuan Jia. Research on Using Genetic Algorithms to Optimize Elman Neural Networks. Neural Computing and Applications, 2013, 23(2):293-297.(SCI, EI)

[7] Hua-juan Huang, Shi-fei Ding, Zhong-zhi Shi. Primal least squares twin support vector regression. Journal of Zhejiang University SCIENCE C, 2013, 14(9):722-732. (SCI, EI)

[8] Shifei Ding, Youzhen Han, Junzhao Yu, Yaxiang Gu. A fast fuzzy support vector machine based on information granulation. Neural Computing and Applications, 2013, 23(suppl 1):S139-S144(SCI, EI)

[9] 黄华娟,丁世飞. 多项式光滑孪生支持向量回归机. 微电子学与计算机, 2013, 30(10):5-8.

[10] 丁世飞,黄华娟. 加权光滑CHKS孪生支持向量机. 软件学报, 2013, 24(11):2548-2557.

[11] 贾洪杰,丁世飞.基于邻域粗糙集约减的谱聚类算法.南京大学学报.自然科学版,2013, 49(5):619-627.

[12] Hong Zhu,Shifei Ding, Xinzheng Xu, Li Xu. A parallel attribute reduction algorithm based on Affinity Propagation clustering. Journal of Computers, 2013, 8(4):990-997. (EI)

[13] Hong Zhu, Shifei Ding, Han Zhao, Lina Bao. Attribute granulation based on attribute discernibility and AP algorithm. Journal of Software, 8(4):834-841.(EI)

[14] Yanan Zhang, Shifei Ding, Xinzheng Xu, Han Zhao, Wanqiu Xing. An Algorithm Research for Prediction of Extreme Learning Machines Based on Rough Sets. Journal of Computers, 2013, 8(5): 1335-1342.(EI)

[15] Hui Li, Shifei Ding. A Novel Neural Network Classification Model based on Covering and Affinity Propagation Clustering Algorithm. Journal of Computational Information Systems, 2013, 9(7):2565-2573. (EI)

[16] Shifei Ding, Junzhao Yu, Huajuan Huang, Han Zhao. Twin Support Vector Machines Based on Particle Swarm Optimization. Journal of Computers, 2013, 8(9): 2296-2303. (EI)

[17] Huajuan Huang,Shifei Ding, Fulin Wu. Invasive Weed Optimization Algorithm for Optimizating the Parameters of Mixed Kernel Twin Support Vecotr Machines. Journal of Computers, 2013, 8(8): 2077-2084. (EI)

[18] Hongjie Jia, Shifei Ding, Hong Zhu, Fulin Wu, Lina Bao. A Feature Weighted Spectral Clustering Algorithm Based on Knowledge Entropy. Journal of Software, 2013, 8(5): 1101-1108. (EI)

[19] Tongfeng Sun, Shifei Ding, Zihui Ren Novel Image Recognition Based on Subspace and SIFT. Journal of Software, 2013, 8(5): 1109-1116.(EI)

[20] Shifei Ding, Fulin Wu, Ru Nie, Junzhao Yu, Huajuan Huang. Twin Support Vector Machines Based on Quantum Particle Swarm Optimization. Journal of Software, 2013, 8(7): 1743-1750. (EI)

[21] Ding Shifei, Zhang Yanan, Xu Xinzheng, Bao Lina. A novel extreme learning machine based on hybrid kernel function. Journal of Computers,2013, 8(8):2110-2117.(EI)

[22] Shifei Ding, Huajuan Huang, Ru Nie. Forecasting Method of Stock Price Based on Polynomial Smooth Twin Support Vector Regression. Lecture Notes in Computer Science, 2013, Volume 7995, 2013, pp 96-105. (EI)

2012年

[1]Shifei Ding, Hong Zhu,Weikuan Jia,Chunyang Su. A survey on feature extraction for pattern recognition.Artificial Intelligence Review,2012, 37(3):169-180. (SCI, EI)

[2] Shifei Ding,Li Xu,Chunyang Su,Fengxiang Jin. An optimizing method of RBF neural network based on genetic algorithm. Neural Computing and Applications, 2012, 21(2):333-336. (SCI, EI)

[3] Shifei Ding,Bingjuan Qi. Research Of granular support vector machine. Artificial Intelligence Review, 2012, 38(1):1-7. (SCI, EI)

[4] Xin-zheng XU, Shi-fei DING, Zhong-zhi SHI, Hong ZHU. Optimizing radial basis function neural network based on rough sets and affinity propagation clustering algorithm. Journal of Zhejiang University-SCIENCE C (Computers & Electronics), 2012,13(2):131-138. (SCI, EI)

[5] Bingjuan Qi,Shifei Ding, Huajuan Huang, Junzhao Yu. A Support Vector Extraction Method based on Clustering Membership.International Journal of Digital Content Technology and its Applications, 2012, 6(13):1-10. (EI)

[6] Chang Tong, Shi-fei Ding, Hong Zhu, Hongjie Jia. A Granularity Attribute Reduction Algorithm Based on Binary Discernibility Matrix. International Journal of Advancements in Computing Technology, 2012, 4(12):213-221. (EI)

[7] Xiaopeng Hua, Shifei Ding. Matrix Pattern Based Projection Twin Support Vector Machines. International Journal of Digital Content Technology and its Applications, 2012, 6(20):172-181. (EI)

[8] Junzhao Yu, Shifei Ding, Huajuan Huang. Twin Support Vector Machines Based on Rough Sets. International Journal of Digital Content Technology and its Applications, 2012, 6(20):493-500. (EI)

[9] Huajuan Huang, Shifei Ding. A Novel Granular Support Vector Machine Based on Mixed Kernel Function. International Journal of Digital Content Technology and its Applications, 2012, 6(20):484-492. (EI)

[10] Shifei Ding(Guest editorial). Special Issue: Advances in Information and Computers, Journal of Computers, 2012, 7(10):2351-2353.(EI)

[11] Shifei Ding(Guest editorial). Special Issue: Advances in Information and Networks. Journal of Networks, 2012, 7(7):1007-1008.(EI)

(被EI收录, 收录号:20123415368412)

[12] Shifei Ding(Guest editorial). Special Issue: Advances in Information and Networks. Journal of Software, 7(9):1923-1924. (EI)

[13] Shifei Ding, Zhentao Yu (Guest editorial). Special Issue: Advances in Computers and Electronics Engineering. Journal of Computers, 2012, 7(12):2851-2852. (EI)

[14]丁世飞, 朱红, 许新征, 史忠植. 基于熵的模糊信息测度研究. 计算机学报, 2012.35(4):796-801(EI).

[15] 朱红,丁世飞, 许新征. 基于改进属性约简的细粒度并行AP聚类算法. 计算机研究与发展, 2012, 49(12):2638-2644 (EI)

[16] 许新征,丁世飞,史忠植,赵作鹏,朱红.一种基于QPSO的脉冲耦合神经网络参数的自适应确定方法. 模式识别与人工智能, 2012,25(6): 909-915(EI)

[17] 马刚,丁世飞, 史忠植. 基于极速学习的粗糙RBF神经网络. 微电子学与计算机, 2012, 29(8):9-14.

2011年

[1]Shifei Ding, Weikuan Jia, Chunyang Su, et al. Research of Neural Network Algorithm Based on Factor Analysis and Cluster Analysis. Neural Computing and Applications, 2011, 20(2): 297-302 (SCI,EI).

[2]Shifei Ding, Chunyang Su, Junzhao Yu. An Optimizing BP Neural Network Algorithm Based on Genetic Algorithm. Artificial Intelligence Review, 2011, 36Algorithm. Artificial Intelligence Review, 2011, 36(2): 153-162 (SCI, EI).

[3]Shifei Ding, Weikuan Jia, Chunyang Su, et al. Research of Neural Network Algorithm Based on Factor Analysis and Cluster Analysis. Neural Computing and Applications, 2011, 20(2): 297-302 (SCI, EI).

[4]Shifei Ding, Chunyang Su, Junzhao Yu. An Optimizing BP Neural Network Algorithm Based on Genetic Algorithm. Artificial Intelligence Review, 2011, 36(2): 153-162 (SCI, EI).

[5]Ding Shifei, Qian Jun, Xu Li, Zhao Xiangwei, Jin Fengxiang. A Clustering Algorithm Based on Information Visualization. International Journal of Digital Content Technology and its Applications, 2011, 5(1): 26-31 (EI).

[6]Shifei Ding, Yu Zhang, Li Xu, Jun Qian. A Feature Selection Algorithm Based on Tolerant Granule. Journal of Convergence Information Technology, 2011, 6(1): 191-195 (EI).

[7]Ding Shifei, Li Jianying, Xu Li, Qian Jun. Research Progress of Granular Computing (GrC). International Journal of Digital Content Technology and its Applications, 2011, 5(1): 162-172 (EI).

[8]Ding Shifei, Qian Jun, Xu Li, Zhao Xiangwei, Jin Fengxiang. A Clustering Algorithm Based on Information Visualization.International Journal of Digital Content Technology and its Applications, 2011, 5(1): 26-31 (EI).

[9]Shifei Ding, Yu Zhang, Li Xu, Jun Qian. A Feature Selection Algorithm Based on Tolerant Granule. Journal of Convergence Information Technology, 2011, 6(1): 191-195 (EI).

[10]Ding Shifei, Li Jianying, Xu Li, Qian Jun. Research Progress of Granular Computing (GrC). International Journal of Digital Content Technology and its Applications, 2011, 5(1): 162-172 (EI).

[11]Shifei DING, Jinrong CHEN, Xinzheng XU, Jianying LI. Rough Neural Networks: A review. Journal of Computational Information Systems, 2011, 7(7): 2338-2346(EI).

[12]Shifei Ding, Xinzheng Xu, Hong Zhu. Studies on Optimization Algorithms for Some Artificial Neural Networks Based on Genetic Algorithm (GA). Journal of Computers, 2011, 6 (5):939-946 (EI).

[13]Shifei DING, Yaxiang GU. A Fuzzy Support Vector Machine Algorithm with Dual Membership Based on Hypersphere. Journal of Computational Information Systems, 2011, 7(6): 2028-2034 (EI).

[14]丁世飞, 齐丙娟, 谭红艳. 支持向量机理论与算法研究综述. 电子科技大学学报,2011, 40(1): 2-10 (EI).

[15] 贾伟宽, 丁世飞, 许新征, 苏春阳, 史忠植. 基于Shannon熵的因子特征提取算法研究. 模式识别与人工智能, 2011, 24(3): 327-331 (EI).

2010年以前

[1] Shifei Ding, Weikuian Jia, Xinzheng Xu, et al. Neural Networks Algorithm Based on Factor Analysis. Lecture Notes in Computer Science, Vol.6063/2010, pp.319-324 (EI).

[2] Shifei Ding, Weikuan Jia, Chunyang Su, et al. An improved BP Neural Netwok Algorithm Based on Factor Analysis. Journal of Convergence Information Technology, 2010, 5(4): 103-108 (EI).

[3] Shifei Ding, Li Xu, Hong Zhu, Liwen Zhang. Research and Progress of Cluster Algorithms based on Granular Computing. International Journal of Digital Content Technology and its Applications, 2010, 4(5): 96-104 (EI).

[4] Shifei Ding, Li Xu, Chunyang Su, Hong Zhu. Using Genetic Algorithms to Optimize Artificial Neural Networks, Journal of Convergence Information Technology, 2010, 5(8): 54-62 (EI).

[5] Shifei Ding, Yongping Zhang, Xiaofeng Lei et al. Research on a principal components decision algorithm based on information entropy. Journal of Information Science, 2009, 35(1):120-127 (SCI, EI).

[6]Shifei Ding, Chunyang Su, Weikuan Jia, Fengxiang Jin, Zhongzhi Shi. Several Progress of Semi-Supervised Learning. Journal of Information & Computational Science, 2009, 6(1): 211-217 (EI).

[7] Shi-Fei Ding, Shi-Xiong Xia, Feng-Xiang Jin, Zhong-Zhi Shi. Novel Fuzzy Information Proximity Measures. Journal of Information Science, 2007, 33 (6):678-685 (SCI, EI).

[8] Ding Shifei, Shi Zhongzhi. Supervised Feature Extraction Algorithm Based on Improved Polynomial Entropy. Journal of Information Science, 32(4): 309-315,2006.8 (SCI, EI)

[9] Ding Shifei, Shi Zhongzhi. Studies on Incidence Pattern Recognition Based on Information Entropy. Journal of Information Science, 31(6):497-502,2005.12 (SCI, EI).

[10] Ding Shifei, Jin Fengxiang. Information characteristics of discrete K-L transform based on information entropy. Transactions Nonferrous Metals Society of China, 2003.6(SCI ,EI).

[11] Shifei Ding, Zhongzhi Shi, Xiaoying Wang. Symmetric Cross Entropy and Information Feature Compression Algorithm. Journal of Computational Information Systems, 1(2): 247-252 , 2005.6 (EI).

[12] Ding Shifei, Shi Zhongzhi. Studies on Information Clustering Algorithm Based on MID. Chinese Journal of Electronics, Vol.15 No.4A, pp.918-920, 2006 (SCI, EI).

[13] Ding Shifei, Shi Zhongzhi. Divergence-based Supervised Information Feature Compression Algorithm.Lecture Notes in Computer Science, Vol. 3971/2006, pp. 1421-1426(SCI, EI).

[14] Shifei Ding, Zhongzhi Shi. A Novel Supervised Information Feature Compression Algorithm. Lecture Notes in Computer Science, Vol. 3991/2006, pp. 777-780 (SCI, EI).

[15] Shifei Ding, Zhongzhi Shi, Yuncheng Wang,and Fengxiang Jin. Optimization Feature Compression and FNN Realization. Lecture Notes in Control and Information Science, Vol. 344/2006, pp. 951-956(SCI, EI).

[16] Shifei Ding, Zhongzhi Shi, and Fengxiang Jin. Supervised Feature Extraction Algorithm Based on Continuous Divergence Criterion. Lecture Notes in Artificial Inteligence, Vol. 4114/2006, pp.268-277 (SCI, EI).

[17] 丁世飞, 贾伟宽, 许新征, 苏春阳. 基于PLS的Elman神经网络算法研究. 电子学报, 2010, 38(2A): 71-75 (EI).

[18] 许新征, 丁世飞, 史忠植, 贾伟宽. 图像分割的新理论何新方法. 电子学报, 2010, 38(2A): 76-82(EI).

[19] 丁世飞,靳奉祥. Fuzzy-Grey信息集成模式识别算法的研究. 计算机辅助设计与图形学学报, 2004, 16(3):275-278 (EI).

[20] 丁世飞,靳奉祥,史忠植. 基于PLS的信息特征压缩算法. 计算机辅助设计与图形学学报, 2005, 17(2):368-371 (EI).

[21] 丁世飞,史忠植. 基于广义距离的直接聚类算法研究.计算机研究与发展，2007, 44(4): 674-679(EI).

[22] 丁世飞,黄华娟. 加权光滑CHKS孪生支持向量机. 软件学报, 2013, 24(11):2548-2557(EI).

获奖情况

1. 2007年获全国优秀博士学位论文提名奖

2. 2006年获山东省优秀博士学位论文奖

3. 2007年获山东高等学校优秀科研成果二等奖，第1位

4. 2006年获中国科学院计算技术研究所优秀博士后出站报告

4. 2004年获山东高等学校优秀科研成果二等奖，第1位

5. 2001年获山东省省级教学成果三等奖，第4位

6. 2016年获江苏省高等学校自然科学二等奖，第1位