丁世飞，男，工学博士，中国科学院计算所博士后，山东省青岛市人，就职于中国矿业大学，位于江苏省徐州市大学路1号。

丁世飞，男，工学博士，中国科学院计算所博士后，祖籍山东省青岛市，就职于中国矿业大学、中国科学院计算技术研究所。社会兼职：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。

担任下列专家委员会委员：　

（1）中国计算机学会杰出会员

（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）中国计算机学会大数据专家委员会委员

（6）中国人工智能学会心智计算专委会副主任委员

（7）中国人工智能学会知识工程与分布智能专委会常委委员

（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）中国人工智能学会机器学习专委会委员

（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）中国人工智能学会会士

担任下列国际期刊编委：

（1）《International Journal of Collaborative Intelligence》主编

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

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）《IEEE TPAMI》

（2）《IEEE TKDE》

（3）《IEEE TNNLS》

（4）《IEEE TIP》

（5）《IEEE TFS》

（6）《ACM TKDD》

（7）《ACM TOIS》

（8）《IEEE/ACM TCBB》

（9）《Pattern Recognition》

（10）《Information Sciences》

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

（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

人工智能与模式识别

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

孪生支持向量机

复杂数据的图机器学习

图聚类分析

多模态学习

多视角学习

情感分析与计算

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)

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.人工智能（第3版-微课视频版），北京：清华大学出版社，2020

5. 丁世飞编著. 人工智能导论（第3版），电子工业出版社，2021

6.丁世飞编著. 人工智能（第4版），电子工业出版社，2024

7. 丁世飞编著. 高级人工智能. 徐州:中国矿业大学出版社, 2015

8. 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

2024年科研成果

国内期刊

[1]张成龙,丁世飞,郭丽丽,张健.随机配置网络研究进展.软件学报, 2024,35(5):2379-2399.

[2]丁世飞,孙玉婷,梁志贞,郭丽丽,张健,徐晓. 弱监督场景下的支持向量机算法综述.计算机学报, 2024, 47(5):987-1009.

[3]杜威,丁世飞,郭丽丽,张健,丁玲.基于价值函数分解和通信学习机制的异构多智能体强化学习方法.计算机学报, 2024, 47(6):1304-1322.

[4]丁世飞,杜威,张健,郭丽丽,丁玲.多智能体深度强化学习研究进展.计算机学报, 2024, 47(7):1547-1567.

国际期刊：

[1]Hui Tu, Shifei Ding, Xiao Xu, Haiwei Hou, Chao Li, Ling Ding. Non-iterative border-peeling clustering algorithm based on swap strategy.InformationSciences, 2024, 654:119864.

(WOS:001111956700001)

[2]Shifei Ding, Qidong Wang, Lili Guo, Jian Zhang, Ling Ding. A novelimage denoising algorithm combining attention mechanism and residual UNet network.Knowledge and Information Systems, 2024, 66(1):581-611.

(WOS:001061540600002)

[3]Shifei Ding,Wei Du, Ling Ding, Jian Zhang, Lili Guo, Bo An. Robust Multi-agent Communication withGraph Information Bottleneck Optimization. IEEETransactions on Pattern Analysis and Machine Intelligence, 2024, 46(5):3096-3107.

(WOS:001196751500062)

[4]ShifeiDing,Qidong Wang, Lili Guo, Xuan Li, Ling Ding, Xindong Wu.Wavelet and AdaptiveCoordinate Attention Guided Fine-grained Residual Network for Image Denoising. IEEETransactions on Circuits and Systems for Video Technology, 2024, 34(7):6156-6166.

(WOS:001263608800004)

[5]Lili Guo, Yikang Song, Shifei Ding. Speaker-aware cognitive networkwith cross-modal attention for multimodal emotion recognition in conversation. Knowledge-Based Systems, 2024, 111969

(WOS:001264169300001)

[6]Lili Guo, Jianglan Zhu, Chenglong Zhang, Shifei Ding.IntuitionisticFuzzy Stochastic Configuration Networks for Solving Binary ClassificationProblems. IEEETransactions on Fuzzy Systems, 2024, 32(8):4210-4219.

(WOS:001291157800026)

2023年科研成果

国内期刊

[1]王丽娟,丁世飞,夏菁.基于多样性的多视图低秩稀疏子空间聚类算法.智能系统学报, 2023,18(2):399-408.

[2]徐清海,丁世飞,孙统风,张健,郭丽丽.改进的基于多路径特征的胶囊网络.计算机应用 2023, 43(5):1330-1335.

[3]丁世飞,张子晨,郭丽丽,张健,徐晓.孪生支持向量回归机研究进展.电子学报,2023,51 (4):1117-1134.

[4]丁世飞,杜威,张健,郭丽丽,徐晓. 基于双评论家的多智能体深度确定性策略梯度方法.计算机研究与发展, 2023,60(10):2394-2404.

[5]丁世飞,张成龙,郭丽丽,张健,丁玲.基于M-estimator函数的加权深度随机配置网络.计算机学报, 2023,46(11):2476-2487.

国际SCI期刊：

[1]Xiangnan Liu, Shifei Ding, Xiao Xu, Lijuan Wang.Deep manifold regularized semi-nonnegative matrixfactorization for Multi-view Clustering. Applied Soft Computing, 2023, 132:109806.

(WOS:000923078700002)

[2]ShifeiDing, Chao Li, Xiao Xu, Ling Ding, Jian Zhang, Lili Guo, Tianhao Shi. ASampling-Based Density Peaks Clustering Algorithm for Large-Scale Data. Pattern Recognition, 2023,136:109238.

(WOS:000900808400008)

[3]Shifei Ding, Wei Du, Xiao Xu, Tianhao Shi, Yanru Wang, Chao Li. An improved densitypeaks clustering algorithm based on natural neighbor with a merging strategy. InformationSciences, 2023, 624:252-276.

(WOS:000915818200001)

[4]Shifei Ding, Wei Du, LingDing, Lili Guo, Jian Zhang, Bo An. Multi-agent dueling Q-learning with meanfield and value decomposition. PatternRecognition, 2023, 139:109436.

(WOS:000946087600001)

[5] Shifei Ding, Wei Du, ChaoLi, Xiao Xu, Lijuan Wang, Ling Ding. Density peaks clustering algorithm basedon improved similarity and allocation strategy. International Journal of Machine Learning and Cybernetics,, 2023, 14(4): 1527-1542.

(WOS:000886330200002)

[6]Haiwei Hou, Shifei Ding, Xiao Xu, Ling Ding. A novel clustering algorithm based on multi-layerfeatures and graph attention networks. Soft Computing, 2023, 27(9):5553-5566.

(WOS:000922539000001)

[7]Jian Zhang, Qinghai Xu, Lili Guo, Ling Ding, Shifei Ding. A novelcapsule network based on deep routing and residual learning. Soft Computing, 2023, 27(2):7895-7906.

(WOS:000957333100005)

[8]Shifei Ding, Chenglong Zhang, Jian Zhang, Lili Guo and Ling Ding.Incremental Multi-Layer Broad Learning System with Stochastic Configuration Algorithmfor Regression. IEEETransactions on Cognitive and Developmental Systems, 2023,15(2):877-886.

(WOS:001005746000049)

[9]ShifeiDing, Yuting Sun, Jian Zhang, Lili Guo, Xiao Xu, Zichen Zhang. Maximum densityminimum redundancy based hypergraph regularized support vector regression. International Journal of Machine Learning and Cybernetics, 2023,14(5): 1933-1950.

(WOS:000903582100001)

[10]LiliGuo, Yanru Wang, Fanchao Wang, Ling Ding, Shifei Ding. Widely-activated networkmerging perceptual loss via discrete wavelet transform for imagesuper-resolution. InternationalJournal of Machine Learning and Cybernetics, 2023, 14(8):2793-2813.

(WOS:000939762200001)

[11]Shifei Ding, Benyu Wu, Xiao Xu, Lili Guo, Ling Ding. Graph clustering network with structure embeddingenhanced. Pattern Recognition, 2023, 144:109833.

(WOS:001057454200001)

[12]Chao Li, Shifei Ding, Xiao Xu, Haiwei Hou, Ling Ding.Fast DensityPeaks Clustering Algorithm Based on Improved Mutual K-nearest-neighbor andSub-cluster Merging. Information Sciences, 2023,647:119470.

(WOS:001062207400001)

[13]Wei Du, Shifei Ding, Chenglong Zhang, Zhongzhi Shi. MultiagentReinforcement Learning With Heterogeneous Graph Attention Network. IEEE Transactions on Neural Networks andLearning Systems, 2023,34(10):6851-6860.

(WOS:000881975400001)

[14]Xiao Xu, Qidong Wang, Lili Guo, Jian Zhang, Shifei Ding. FEMRNet:Feature-enhanced multi-scale residual network for image denoising. AppliedIntelligence, 2023, 53 (21):26027-26049.

(WOS:001049168300003)

[15]XiaoXu, Haiwei Hou, Shifei Ding. Semi-supervised deep density clustering. Applied Soft Computing, 2023, 148:110903.

(WOS:001093167500001)

2022年科研成

国内期刊

[1]刘相男,丁世飞,王丽娟.基于深度图正则化矩阵分解的多视图聚类.智能系统学报,2022,17(1):158-169.

[2]王凡超,丁世飞.基于广泛激活深度残差网络的图像超分辨率重建.智能系统学报,2022,17(2):440-446.

[3]许新征,常建英, 丁世飞.基于StarGAN和类别编码器的图像风格转换 软件学报, 2022, 33(4):1516-1526

[4]徐晓,丁世飞,丁玲.密度峰值聚类研究进展.软件学报, 2022,33(5):1800-1816.

[5]侯海薇,丁世飞,徐晓.基于无监督表征学习的深度聚类研究进展. 模式识别与人工智能, 2022, 35(11):999-1014.

国际SCI期刊：

[1]Shifei Ding, Zichen Zhang, Yuting Sun, Songhui Shi. Multiple BirthSupport Vector Machine Based on Dynamic Quantum Particle Swarm OptimizationAlgorithm. Neurocomputing, 2022, 480:146-156.

(WOS:000761796800012)

[2]Yuting Sun, Shifei Ding, Lili Guo, Zichen Zhang. HypergraphRegularized Semi-supervised Support Vector Machine. Information Sciences, 2022,591:400-421.

(WOS:000768227800006)

[3]Yanru Wang, Shifei Ding, Lijuan Wang, Shuying Du. A manifoldp-spectral clustering with sparrow search algorithm. Soft Computing, 2022, 26(4): 1765 -1777.

(WOS:000743851100004)

[4]Chenglong Zhang, Shifei Ding, Wei Du. Broad Stochastic ConfigurationNetwork for Regression. Knowledge-BasedSystems, 2022, 243:108403.

(WOS:000788139700001)

[5]Shifei Ding, Zichen Zhang, Lili Guo, Yuting Sun. An optimized twinsupport vector regression algorithm enhanced by ensemble empirical modedecomposition and gated recurrent unit. Information Sciences, 2022, 598:101-125.

(WOS:000783324300007)

[6]Yuting Sun,Shifei Ding,Zichen Zhang,Chenglong Zhang. Hypergraph based semi‑supervised support vector machine for binary andmulti‑category classifcations.International Journal of Machine Learning and Cybernetics, 2022,13(5):1369-1386.

(WOS:000716302200001)

[7]Haiwei Hou, Shifei Ding, Xiao Xu. A deep clustering by multi-levelfeature fusion. InternationalJournal of Machine Learning and Cybernetics, 2022, 13(10): 2813-2823. (WOS:000785947000001)

[8]GuangcongSun, Shifei Ding. Tongfeng Sun, Chenglong Zhang, Wei Du. A novel dense capsulenetwork based on dense capsule layers. AppliedIntelligence, 2022,52(3):3066-3076. (DOI:10.1007/s10489-021-02630-w)

(WOS:000667670900001)

[9]Chenglong Zhang, Shifei Ding, Lili Guo, Jian Zhang. Broad LearningSystem Based Ensemble Deep Model. SoftComputing, 2022, 26(15):7029-7041.

(WOS:000780850000003)

[10]Jian Zhang, Shifei Ding, Tongfeng Sun, Lili Guo. A Gaussian RBM with binary auxiliary units. International Journal of Machine Learning and Cybernetics, 2022,13(9):2425–2433.

(WOS:000766431600001)

[11]Chao Li, Shifei Ding, Xiao Xu, Shuying Du, Tianhao Shi. Fast densitypeaks clustering algorithm in polar coordinate system. Applied Intelligence, 2022, 52(12):14478-14490.

(WOS:000766072200005)

[12]Wei Du, Shifei Ding, Lili Guo, Jian Zhang, Chenglong Zhang, LingDing. Value function factorization with dynamic weighting for deep multi-agentreinforcement learning. InformationSciences, 2022, 615:191-208.

(WOS:000890939400011)

2021年科研成果

国内期刊：

[B1]于文家,丁世飞.基于自注意力机制的条件生成对抗网络.计算机科学, 2021,48(1): 241-246.

[B2]邵长龙,孙统风,丁世飞.基于信息熵加权的聚类集成算法.南京大学学报(自然科学版), 2021, 57(2):189-196.

[B3]杜淑颖,侯海薇,丁世飞. 基于多层次特征的深度集成聚类算法.南京大学学报(自然科学版),2021, 57(4):575-581.

[B4]王丽娟,丁世飞.一种基于ELM-AE特征表示的谱聚类算法.智能系统学报,2021, 16(3):560-566.

[B5]杜淑颖,施天豪,丁世飞.基于电子分层模型和凝聚策略的密度峰值聚类.南京理工大学学报, 2021,45(4):385-393.

[B6]丁玲,丁世飞,张健,张子晨.使用VGG能量损失的单图像超分辨率重建.软件学报,2021, 32(11):3659-3668.

[B7]张健,丁世飞,丁玲,张成龙.基于实值RBM的深度生成网络研究.软件学报,2021, 32(12):3802-3813.

国际SCI期刊：

[1]Guangcong Sun, Shifei Ding, Tongfeng Sun, Chenglong Zhang. SA-CapsGAN:Using Capsule Networks With Embedded Self-Attention For Generative AdversarialNetwork. Neurocomputing, 2021,423:399-406.

(WOS:000599909500017)

[2]Zichen Zhang, Shifei Ding, Yuting Sun. MBSVR:Multiple birth support vector regression. InformationSciences, 2021, 552:65-79.

(WOS:000612175800005)

[3]Xiao Xu, Shifei Ding, Yanru Wang, Lijuan Wang, Weikuan Jia. A fastdensity peaks clustering algorithm with sparse search. InformationSciences, 2021, 554:61-83.

(WOS:000617760500005)

[4]LingDing, Shifei Ding, Yangru Wang, Lijuan Wang, Hongjie Jia. M-pSC: a manifoldp-spectral clustering algorithm. InternationalJournal of Machine Learning and Cybernetics, 2021, 12(2): 541-553.

(WOS:000559653200001)

[5]Yuting Sun, Shifei Ding, Zichen Zhang,Weikuan Jia. An improved grid search algorithm to optimize SVR for prediction. Soft Computing, 2021, 25(7):5633-5644.

(WOS:000609081500001)

[6]ChenglongZhang, Shifei Ding, Jian Zhang, Weikuan Jia. Parallel Stochastic ConfigurationNetworks for Large-scale Data Regression. Applied SoftComputing, 2021, 103:107143.

(WOS:000635181400013)

[7]ChenglongZhang, Shifei Ding. A Stochastic Configuration Network Based on Chaotic SparrowSearch Algorithm. Knowledge-Based Systems, 2021, 220:106924.

(WOS:000637681300003)

[8]YuruZhang, Shifei Ding, Lijuan Wang, Yanru Wang, Ling Ding. Chameleon algorithmbased on mutual k-nearest neighbors. Applied Intelligence, 2021, 51(4):2031-2044.

(WOS:000582088700003)

[9] Lijuan Wang, Shifei Ding, Yanru Wang,Ling Ding. A robust spectralclustering based on grid-partition and decision-graph. International Journal of MachineLearning and Cybernetics, 2021, 12(5):1243-1254.

(WOS:000591203700001)

[10] YanruWang, Shifei Ding, Lijuan Wang, Ling Ding. An Improved Density-based Adaptivep-spectral Clustering Algorithm. International Journal ofMachine Learning and Cybernetics, 2021, 12(6):1571-1582.

(WOS:000591228100001)

[11] Wei Du,Shifei Ding. A survey on multi-agent deep reinforcement learning: from theperspective of challenges and applications. Artificial Intelligence Review, 2021,54(5):3215-3238.

(WOS:000592138500001)

[12]Zhengfan Chen, Shifei Ding, Haiwei Hou. A novel self-attention deepsubspace clustering, InternationalJournal of Machine Learning and Cybernetics, 2021, 12(8):2377–2387.

(WOS:000640957300002)

[13]TianhaoShi, Shifei Ding, Xiao Xu, Ling Ding. A community detection algorithm based onQuasi-Laplacian centrality peaks clustering. AppliedIntelligence, 2021,51(11):7917-7932.

(WOS:000630684900004)

[14] YuruZhang, Shifei Ding, Yanru Wang, Haiwei Hou. Chameleon algorithm based onimproved natural neighbor graph generating sub-clusters. Applied Intelligence, 2021,51(11):8399-8415.

(WOS:000636634600002)

2020年科研成果

国内期刊

[1]杜鹏,丁世飞.基于混合词向量深度学习模型的DGA域名检测方法. 计算机研究与发展, 2020,57(2):433-446.

[2]丁世飞,徐晓,王艳茹.基于不相似性度量优化的密度峰值聚类算法.软件学报,2020,31(11):3321-3333.

[3]王丽娟,丁世飞,丁玲.基于迁移学习的软子空间聚类算法[J]. 南京大学学报(自然科学版), 2020, 56(4):515-523.

[4]史颂辉,丁世飞.基于能量的结构化最小二乘孪生支持向量机[J].智能系统学报, 2020, 15(5):1013-1019.

[5]陈力,丁世飞,于文家. 基于跨通道交叉融合和跨模块连接的轻量级卷积神经网络.计算机应用, 2020,40(12):3451-3457.

[6]夏菁,丁世飞.基于低秩和稀疏约束的自权重多视角子空间聚类.南京大学学报(自然科学版), 2020, 56(6):862-869.

国际SCI期刊：

[1]Nan Zhang,Shifei Ding, Tongfeng Sun, Hongmei Liao, Zhongzhi Shi. Multi-view RBM withposterior consistency and domain adaptation. InformationSciences, 2020, 516:142-157.

(SCI:000515432200009)

[2]JianZhang, Shifei Ding, Nan Zhang, Weikuan Jia. Adversarial training methods forBoltzmann Machines. IEEE Access, 2020,8:4594-4604.

(SCI:000532709700001)

[3]XiaoyuWang, Shifei Ding, Weikuan Jia. Active constraints spectral clustering based onHessian matrix. Soft Computing, 2020,24(3):2381-2390.

(SCI:000518595800050)

[4]ShifeiDing, Songhui Shi, Weikuan Jia. Research onfingerprint classification based on twin support vector machine. IET Image Processing,2020, 14(2):231-235.

(SCI:000510220400003)

[5] SonghuiShi, Shifei Ding. Energy-based structural least squares MBSVM forclassification. Applied Intelligence, 2020,50(3):681-697.

(SCI:000515003200003)

[6] Jian Zhang, ShifeiDing, Weikuan Jia. An adversarial non-volume preserving flow model withBoltzmann priors. International Journal of Machine Learningand Cybernetics, 2020, 11(4): 913-921.

(SCI:000518731400013)

[7] NanZhang, Shifei Ding, Jian Zhang, Xingyu Zhao. Robust spike-and-slab deepBoltzmann machines for face denoising. Neural Computingand Applications, 2020, 32(7):2815-2827.

(SCI:000522553100064)

[8] Shifei Ding, YutingSun, Yuexuan An, Weikuan Jia. Multiplebirth support vector machine based on recurrent neural networks. AppliedIntelligence, 2020, 50(7):2280-2292. (DOI:10.1007/s10489-020-01655-x).

(SCI:000518068400001)

[9] Haitian Zhang,Shifei Ding, Weikuan Jia. Ensemble Adaptation Networks with low-costunsupervised hyper-parameter search. Pattern Analysis and Applications, 23(3):1215-1224.

(SCI:000540607300011)

[10] Yunxin Liu, Shifei Ding,Weikuan Jia. A Novel Prediction Method of Complex Univariate Time-series Basedon K-means Clustering. Soft Computing, 2020,24(21):16425-16437.

(SCI:000528425500004)

[12]Zichen Zhang, ShifeiDing, Yuting Sun. A support vector regression model hybridized with chaotickrill herd algorithm and empirical mode decomposition for regression task. Neurocomputing, 2020, 410:185-201.

(SCI:000579799300017)

[13] Xiao Xu, ShifeiDing, Lijuan Wang, Yanru Wang. A robust density peaks clustering algorithm withdensity-sensitive similarity. Knowledge-Based Systems, 2020, 200, 106028.

(SCI:000537319700003)

[14] Yuru Zhang, Shifei Ding, Lijuan Wang, Yanru Wang, Ling Ding. Chameleonalgorithm based on mutual k-nearest neighbors. Applied Intelligence, 2020 (DOI:10.1007/s10489-020-01926-7)

(SCI:000582088700003)

[15] Lijuan Wang, ShifeiDing. Multi-view Spectral Clustering via ELM-AE Ensemble FeaturesRepresentations Learning. IEEE Access, 2020, 8:198679-198690.

(SCI:000589775600001)

[16] Lijuan Wang, Shifei Ding, Yanru Wang,Ling Ding. A robust spectralclustering based on grid-partition and decision-graph. International Journal of MachineLearning and Cybernetics, 2020, (DOI: 10.1007/s13042-020-01231-2)

(SCI:000591203700001)

[17] YanruWang, Shifei Ding, Lijuan Wang, Ling Ding. An Improved Density-based Adaptivep-spectral Clustering Algorithm. International Journal ofMachine Learning and Cybernetics, 2020 (DOI:10.1007/s13042-020-01236-x)

(SCI:000591228100001)

2019年科研成果

国内期刊

[1]王小玉,丁世飞.基于共享近邻的成对约束谱聚类算法.计算机工程与应用,2019,55(2): 142-147.

[2]王丽娟,丁世飞,贾洪杰.基于消息传递的谱聚类算法.数据采集与处理,2019,34(3):548-557.

[3]王丽娟,丁世飞.一种粒子群优化的SVM-ELM模型.计算机科学与探索,2019,13(4): 657-665.

[4]卞维新,丁世飞,张楠,张健,赵星宇.结合滤波和深度玻尔兹曼机重构的指纹增强.软件学报,2019, 30(6):1886-1900.

[5]张健,丁世飞,张楠,等.受限玻尔兹曼机研究综述.软件学报,2019, 30(7):2073-2090.

[6]杜威, 丁世飞.多智能体强化学习综述.计算机科学, 2019,46(8):1-8.

[7]秦悦, 丁世飞.半监督聚类综述.计算机科学, 2019,46(9):15-21.

[8]杜淑颖,丁世飞.基于六度分割理论的社交好友推荐算法研究.南京理工大学学报, 2019, 43(4):468-473.

[9]王丽娟,丁世飞.基于聚类核的核极速学习机.南京师范大学学报, 2019,42(3):145-150

[10]张楠,丁世飞,张健,赵星宇.基于噪声数据与干净数据的深度置信网络.软件学报,2019, 30(11):3326−3339.

国际SCI期刊：

[1]XingyuZhao, Shifei Ding, Yuanxuan An, Weikuan Jia. Asynchronous ReinforcementLearning Algorithms for Solving Discrete Space Path Planning Problems. Applied Intelligence, 2018,48(12):4889-4904.

(SCI:000450446800023)

[2]Lin Cong,Shifei Ding, Lijuan Wang, Aijuan Zhang, Weikuan Jia. Image segmentationalgorithm based on superpixel clustering. IET ImageProcessing, 2018, 12(11): 2030-2035.

(SCI: 000454356600013)

[1]Shifei Ding, LinCong, Qiankun Hu, Hongjie Jia, Zhongzhi Shi. A multiway p-spectral clusteringalgorithm. Knowledge-Based Systems, 2019, 164:371-377.

(SCI: 000457508900029)

[2] KaiZeng, Shifei Ding. Single Image Super-Resolution Using a Polymorphic ParallelCNN. Applied Intelligence, 2019,49(1):292-300.

(SCI:000456949900020)

[3]XingyuZhao, Shifei Ding, Yuanxuan An, Weikuan Jia. Applications of asynchronous deepreinforcement learning based on dynamic updating weights. Applied Intelligence, 2019,49(2): 581-591.

(SCI: 000457362600017)

[4] MingjingDu, Shifei Ding, Yu Xue, Zhongzhi Shi. A novel density peaks clustering withsensitivity of local density and density-adaptive metric. Knowledge and Information Systems, 2019,59(2):285-309

(SCI:000461572500002)

[5] Nan Zhang, Shifei Ding,Hongmei Liao, Weikuan Jia. Multimodal Correlation Deep Belief Networks forMulti-view Classification. Applied Intelligence, 2019,49(5): 1925-1936

(SCI: 000463843400017)

[6] Xiao Xu,Shifei Ding, Hui Xu, Hongmei Liao, Yu Xue. A feasible density peaks clusteringalgorithm with amerging strategy. Soft Computing, 2019, 23(13):5171-5183

(SCI: 000469418900040)

[7] Shifei Ding, XingyuZhao, Jian Zhang, Xiekai Zhang, Yu Xue. A review on multi-class TWSVM. Artificial Intellgence Review, 52(2):775-801.

(SCI: 000472573300002)

[8]LijuanWang, Shifei Ding, HongjieJia. An Improvement of Spectral Clustering via Message Passing and Density SensitiveSimilarity. IEEE ACCESS, 7:101054-101062.

(SCI: 000481688500044)

[9] ZichenZhang, Shifei Ding, WeikuanJia. A hybrid optimization algorithm based on cuckoo search and differentialevolution for solving constrained engineering problems. Engineering Applications of Artificial Intelligence, 2019,85:254-268.

(SCI: 000488994300021)

[10]YanruWang, Shifei Ding, Xiao Xu.The multi-tag semantic correlation used for micro-blog user interest modeling. Engineering Applications of Artificial Intelligence, 2019,85:765-772.

(SCI: 000488994300059)

[11] ShifeiDing, Peng Du, Xingyu Zhao, Qiangbo Zhu, Yu Xue. BEMD image fusion based onPCNN and compressed sensing. Soft Computing, 2019,23(20):10045-10054.

(SCI: 000487038100019)

2018年科研成果

国内期刊

[1]丁世飞, 张健,张谢锴, 安悦瑄. 多分类孪生支持向量机研究进展.软件学报, 2018,29(1):89-108.

[2] 丁世飞, 张健,史忠植. 基于权值不确定性的玻尔兹曼机算法.软件学报, 2018,29(4): 1131-1142.

[3] 胡乾坤, 丁世飞.局部相似性优化的p-谱聚类算法.计算机科学与探索, 2018, 12 (3): 462-471.

[4] 侯艳路, 丁世飞,孙统风. 混合深度学习模型C-RF及其在手写数字识别中的应用.数据采集与处理, 33(2):343-350.

[5] 徐晓, 丁世飞,孙统风, 廖红梅.基于网格筛选的大规模密度峰值聚类算法. 计算机研究与发展,2018, 55 (11): 2419-2429.

国际SCI期刊：

[1] Shifei Ding, Hongjie Jia, Yu Xue. A semi-supervisedapproximate spectral clustering algorithm based on HMRF model. InformationSciences, 2018,429:215-228.

(SCI: 000423653300015)

[2] Nan Zhang, Shifei Ding, Jian Zhang, Yu Xue. An overview onRestricted Boltzmann Machines. Neurocomputing,2017,275:1186-1199.

(SCI: 000418370200113)

[3] Weixin Bian, Shifei Ding, Weikuan Jia. Collaborative filteringmodel for enhancing fingerprint image. IET Image Processing, 2018,12(1):149-157.

(SCI: 000419403600018)

[4] Shuyan Fan, Shifei Ding, Yu Xue. Self-adaptive kernel K-meansalgorithm based on the shuffled frog leaping algorithm. Soft Computing, 2018,22(3):861-872.

(SCI: 000423704100014)

[5] Xiao Xu, Shifei Ding, Mining Du, Yu Xue. DPCG: an efficientdensity peaks clustering algorithm based on grid. International Journal ofMachine Learning and Cybernetics,2018, 9(5):743-754.

(SCI:000430559500003)

[6] Shifei Ding, Xiao Xu, Shuyan Fan,Yu Xue. Locally adaptive multiple kernel k-means based on shared nearestneighbors. Soft Computing, 2018,22(14): 4573-4583.

(SCI: 000435598400006)

[7]Mingjing Du, ShifeiDing, Yu Xue. A robust density peaks clustering algorithm using fuzzyneighborhood. International Journal of Machine Learning and Cybernetics, 2018, 9(7):1131-1140.

(SCI:000436014500006)

[8] Shifei Ding, Xingyu Zhao, Hui Xu,Qiangbo Zhu, Yu Xue. NSCT-PCNN image fusion based on image gradient motivation. IET ComputerVision, 2018, 12(4):377-383.

(SCI:000433186700002)

[9] Yuexuan An, Shifei Ding, Songhui Shi,Jingcan Li. Discrete space reinforcement learning algorithm based on supportvector machine classification. Pattern Recognition Letters, 111:30-35.

(SCI:000441141200005)

[10] Weixin Bian, Shifei Ding, Yu Xue.An improved fingerprint orientation field extraction method based on qualitygrading scheme. International Journal of Machine Learning and Cybernetics,2018,9(8): 1249-1260.

(SCI:000438855100001)

[11] Mingjing Du, Shifei Ding, XiaoXu, Yu Xue. Density peaks clustering using geodesic distances. InternationalJournal of Machine Learning and Cybernetics, 2018,9(8):1335-1349.

(SCI:000438855100008)

[12] Xiao Xu, Shifei Ding, Zhongzhi Shi. An improved densitypeaks clustering algorithm with fast finding cluster centers. Knowledge-BasedSystems, 2018, 158:65-74.

(SCI: 000440529200006)

[13] Lingheng Meng, Shifei Ding, Nan Zhang,Jian Zhang. Research of stacked denoising sparse autoencoder. Neural Computingand Applications, 2018, 30(7):2083-2100.

(SCI: 000444953300007)

[14] Jian Zhang, Shifei Ding, Nan Zhang. An overview onprobability undirected graphs and their applications in image processing. Neurocomputing,2018,321:156-168.

(SCI:000447385100015)

[15] Xingyu Zhao, Shifei Ding,Yuexuan An, Weikuan Jia. Asynchronous Reinforcement Learning Algorithms forSolving Discrete Space Path Planning Problems. Applied Intelligence, 2018, 48(12):4889-4904.

(SCI:000450446800023)

[16] Lin Cong, Shifei Ding, Lijuan Wang, Aijuan Zhang, WeikuanJia. Image segmentation algorithm based on superpixel clustering. IET ImageProcessing, 2018, 12(11): 2030-2035.

(SCI: 000454356600013)

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).

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