基本介紹
- 中文名：丁世飛
- 國籍：中國
- 民族：漢族
- 出生地：山東省青島市
- 職業：教師
- 畢業院校：中國科學院計算技術研究所
- 信仰：無
- 主要成就：全國智慧型信息處理學術會議（NCIIP）程式委員會主席
- 代表作品：孿生支持向量機：算法與拓展
- 性別：男
- 單位：中國礦業大學
- 學歷：博士研究生
- 學位：中國科學院計算所博士後
丁世飛.人物簡歷,丁世飛.學術兼職,丁世飛.研究方向,丁世飛.科研項目,已經完成的項目,正在進行的項目,丁世飛.論文著作,已出版著作,已發表論文,丁世飛.獲獎情況,
丁世飛.人物簡歷
姓名：丁世飛
性別：男
學歷：博士研究生
學位：工學博士，中國科學院計算所博士後
祖籍：山東省青島市
地址：江蘇省徐州市大學路1號
現任：
中國科學院計算技術研究所客座研究員、博士生導師
中國礦業大學教授、博士生導師
中國礦業大學計算機套用技術博士點學科帶頭人
中國礦業大學計算機科學與技術學院教授委員會主任
丁世飛.學術兼職
擔任下列專家委員會委員：
（1）中國計算機學會傑出會員、資深會員
（2）中國計算機學會理事
（3）中國人工智慧學會理事
（4）中國計算機學會人工智慧與模式識別專委會委員
（5）中國計算機學會多值邏輯與模糊邏輯專委會常委委員
（6）中國人工智慧學會知識工程與分散式智慧型專業委員會委員
（7）中國人工智慧學會機器學學習專業委員會委員
（8）中國人工智慧學會粒計算與知識發現專業委員會常委委員
（9）江蘇省計算機學會人工智慧專業委員會常委委員
（10）江蘇省計算機學會大數據專家委員會委員
（11）江蘇省人工智慧學會理事
（12）江蘇省人工智慧學會機器學習專委會副主任委員
擔任下列國際期刊編委：
（1）《IJCI: International Journal of Collaborative Intelligence》主編
（2）《JDCTA: Journal of Digital Contents Technology and Application》副主編
（3）《JCIT: Journal of Convergence Information Technology》編委
（4）《AISS: Advances in Information Sciences and Service Sciences》編委
（5）《IJACT: International Journal of Advancements in Computing Technology》編委
（6）《JCP: Journal of Computers》編委
（7）《JSW: Journal of Software》編委
（8）《IPL:CInformation Processing Letters》編委
（9）《AMIS: Applied Mathematics & Information Sciences》編委
擔任下列國際期刊特約編輯：
（1）《Applied Mathematics & Information Sciences》特約編輯(Guest Editor)
（2）《INFORMATION》的特約編輯(Guest Editor)
（3）《Neurocpmputing》特約編輯(Guest Editor)
（4）《The Scientific World Journal》的特約編輯(Guest Editor)
（5）《Mathematical Problems in Engineering》的特約編輯(Guest Editor)
（6）《Journal of Computers (JCP)》特約編輯(Guest Editor)
（7）《Journal of Software (JSW)》特約編輯(Guest Editor)
（8）《Journal of Networks (JNW)》的特約編輯(Guest Editor)
擔任下列國際SCI源刊特約審稿專家：
（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》
擔任下列國核心心期刊審稿專家：
（1）《計算機學報》
（2）《軟體學報》
（3）《計算機研究與發展》
（4）《中國科學》（中英文版）
（5）《電子學報》（中英文版）
（6）《模式識別與人工智慧》
（7）《計算機科學》
（8）《小型微型計算機系統》
（9）《計算機套用研究》
（10）《計算機工程與科學》
（11）《計算機工程與套用》
（12）《計算機科學與探索》
（13）《微電子學與計算機》
擔任下列國內外會議PC Chair or Member：
（1）全國智慧型信息處理學術會議（NCIIP）程式委員會主席
（2）江蘇省人工智慧學術會議程式委員會主席
（3）2012\2013\2014年信息、智慧型與計算國際研討會主席
（4）粒度計算國際會議程式委員會委員
（5）智慧型信息處理國際會議程式委員會委員
（6）中國機器學習會議程式委員會委員
（7）中國粗糙集與軟計算、中國粒計算、中國Web智慧型聯合會議程式委員會委員等
擔任下列項目評審專家：
（1）國家863、973項目評審專家
（2）國家自然科學獎、國家科技進步獎、國家科技發明獎（三大獎）評審專家
（3）國家自然科學基金重點項目、面上項目、青年項目評審專家
丁世飛.研究方向
模式識別與人工智慧
機器學習與數據挖掘
粒度計算與知識發現
感知與認知計算
大數據智慧型分析
神經網路與深度學習
支持向量機與孿生支持向量機
丁世飛.科研項目
已經完成的項目
1. 2001-2003參加並完成國家自然科學基金項目“信息模式識別理論及其在地學中的套用”的研究（項目編號: 40074001）
2. 1999-2001主持完成省教育廳項目“信息模式識別理論及其在害蟲預測預報中的套用研究”
3. 1998-2000主持完成省教育廳項目“農作物病蟲害現代生物數學預報技術研究”
4. 2005-2006主持中國博士後科學基金項目“視感知學習理論及其套用研究”（No.2005037439）
5. 2004-2006主持山東省作物生物學國家重點實驗室開放基金項目“山東省玉米病蟲害數字模式分類的研究”（No.20040010）
6. 2006-2008參加國家自然科學基金項目“多元數據的信息模式研究與地學數據分析”（No.40574001）
7. 2006-2009參加國家863高技術項目“基於感知機理的智慧型信息處理技術”(No. 2006AA01Z128)
8. 2007-2010主持中國科學院智慧型信息處理重點實驗室開放基金項目“基於認知的模式特徵分析理論與算法研究”(No.IIP2006-2)
9. 2010-2012主持江蘇省基礎研究計畫（自然科學基金）項目“面向高維複雜數據的粒度知識發現研究”(No.BK2009093)
10.2011-2012主持北京郵電大學智慧型通信軟體與多媒體北京市重點實驗室開放課題 “粒度SVM方法與套用研究”
11. 2010-2012參加國家自然科學基金項目“分散式計算環境下的並行數據挖掘算法與理論研究”(No.60975039)
12. 2011-2013主持中國科學院智慧型信息處理重點實驗室開放基金項目“高維複雜數據的粒度支持向量機理論與算法研究”(No.IIP2010-1)
正在進行的項目
1. 2013.1-2017.12主持國家重點基礎研究發展計畫（973計畫）課題“腦機協同的認知計算模型”(No.2013CB329502)
2. 2014.1-2017.12主持國家自然科學基金項目“面向大規模複雜數據的多粒度知識發現關鍵理論與技術研究” (No. 61379101)
3. 2017.1-2020.12，主持中央高校基本業務費學科重點項目“基於大數據粒化的多粒多層神經網路及其最佳化方法研究”(No.2017XKZD03)
丁世飛.論文著作
已出版著作
1. 丁世飛,靳奉祥,趙相偉著. 現代數據分析與信息模式識別. 北京:科學出版社,2012
2.丁世飛編著. 人工智慧. 北京: 清華大學出版社, 2010
3. 丁世飛編著.人工智慧（第2版）. 北京: 清華大學出版社, 2015
4. 丁世飛編著. 高級人工智慧. 徐州:中國礦業大學出版社, 2015
5. 丁世飛著. 孿生支持向量機：理論與拓展. 北京: 科學出版社, 2017
已發表論文
2017年
國內期刊
[1]曾凱,丁世飛. 圖像超解析度重建的研究進展. 計算機工程與套用, 2017, 53(16):29-35.
[2]丁世飛,張楠,史忠植. 拉普拉斯極速學習機.軟體學報,2017,28(10):2599-2610.
[3] 丁世飛,黃華娟.最小二乘孿生參數化不敏感支持向量回歸機.軟體學報, 2017, 28(12):3146−3155.
國際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
國內期刊
[1]張謝鍇,丁世飛. 基於馬氏距離的孿生支持向量. 計算機科學, 2016, 43(3):49-53
[2]樊淑炎, 丁世飛. 基於多尺度的改進Graph cut算法. 山東大學學報(工學版), 2016, 46 (1): 28-33
[3]孟令恆, 丁世飛. 基於單靜態圖像的深度感知模型研究. 山東大學學報(工學版), 2016, 46(3):37-43
[4]朱強波,丁世飛. 基於GA最佳化自適應NSCT-PCNN圖像融合.小型微型計算機系統, 2016
37 (7): 1583-1587.
[5]馬恆, 丁世飛. 一種基於混合數據的相似性度量的譜聚類算法. 小型微型計算機系統, 2016, 37 (8): 1751-1754.
[6]王婷婷, 丁世飛. 基於資格跡的RBF非線性強化學習研究. 小型微型計算機系統, 2016, 37 (7): 1508-1512
國際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
國內期刊
[1] 鮑麗娜,丁世飛, 許新征, 孫統風.基於鄰域粗糙集的極速學習計算法. 濟南大學學報.自然科學版, 2015, 29(5):367-371
[2] 花小朋,丁世飛. 基於魯棒局部嵌入的孿生支持向量機. 中南大學學報(自然科學版), 2015, 46(1):149-156
[3] 黃華娟,丁世飛, 史忠植. 光滑CHKS孿生支持向量回歸機.計算機研究與發展, 2015, 52(3): 569-578
[4] 郭麗麗, 丁世飛. 深度學習研究進展. 計算機科學, 2015, 42(5):28-33. [2015.5]
[5] 賈洪傑, 丁世飛, 史忠植. 求解大規模譜聚類的近似加權核k-means算法. 軟體學報, 2015, 26(11):2836−2846
國際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,丁世飛. 局部保持對支持向量機. 計算機研究與發展, 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] 黃華娟,丁世飛. 多項式光滑孿生支持向量回歸機. 微電子學與計算機, 2013, 30(10):5-8.
[10] 丁世飛,黃華娟. 加權光滑CHKS孿生支持向量機. 軟體學報, 2013, 24(11):2548-2557.
[11] 賈洪傑,丁世飛.基於鄰域粗糙集約減的譜聚類算法.南京大學學報.自然科學版,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收錄, 收錄號: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]丁世飛, 朱紅, 許新征, 史忠植. 基於熵的模糊信息測度研究. 計算機學報, 2012.35(4):796-801(EI).
[15] 朱紅,丁世飛, 許新征. 基於改進屬性約簡的細粒度並行AP聚類算法. 計算機研究與發展, 2012, 49(12):2638-2644 (EI)
[16] 許新征,丁世飛,史忠植,趙作鵬,朱紅.一種基於QPSO的脈衝耦合神經網路參數的自適應確定方法. 模式識別與人工智慧, 2012,25(6): 909-915(EI)
[17] 馬剛,丁世飛, 史忠植. 基於極速學習的粗糙RBF神經網路. 微電子學與計算機, 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]丁世飛, 齊丙娟, 譚紅艷. 支持向量機理論與算法研究綜述. 電子科技大學學報,2011, 40(1): 2-10 (EI).
[15] 賈偉寬, 丁世飛, 許新征, 蘇春陽, 史忠植. 基於Shannon熵的因子特徵提取算法研究. 模式識別與人工智慧, 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] 丁世飛, 賈偉寬, 許新征, 蘇春陽. 基於PLS的Elman神經網路算法研究. 電子學報, 2010, 38(2A): 71-75 (EI).
[18] 許新征, 丁世飛, 史忠植, 賈偉寬. 圖像分割的新理論何新方法. 電子學報, 2010, 38(2A): 76-82(EI).
[19] 丁世飛,靳奉祥. Fuzzy-Grey信息集成模式識別算法的研究. 計算機輔助設計與圖形學學報, 2004, 16(3):275-278 (EI).
[20] 丁世飛,靳奉祥,史忠植. 基於PLS的信息特徵壓縮算法. 計算機輔助設計與圖形學學報, 2005, 17(2):368-371 (EI).
[21] 丁世飛,史忠植. 基於廣義距離的直接聚類算法研究.計算機研究與發展，2007, 44(4): 674-679(EI).
[22] 丁世飛,黃華娟. 加權光滑CHKS孿生支持向量機. 軟體學報, 2013, 24(11):2548-2557(EI).
丁世飛.獲獎情況
1. 2007年獲全國優秀博士學位論文提名獎
2. 2006年獲山東省優秀博士學位論文獎
3. 2007年獲山東高等學校優秀科研成果二等獎，第1位
4. 2006年獲中國科學院計算技術研究所優秀博士後出站報告
4. 2004年獲山東高等學校優秀科研成果二等獎，第1位
5. 2001年獲山東省省級教學成果三等獎，第4位
6. 2016年獲江蘇省高等學校自然科學二等獎，第1位
