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Correspondence   Projects   Publications   Resources

  Correspondence

  • Name:   Wei-Jie Chen (Google Scholar)
  • E-mail:   wjcper2008@126.com
  • Address:   Zhijiang College, Zhejiang University of Technology, Hangzhou 310024, China
  • Wei-Jie Chen received his Bachelor's degree and Doctor's degree in College of Information from Zhejiang University of Technology, China, in 2006 and 2011, respectively. Currently, he is an associate professor at the Zhijiang College, Zhejiang University of Technology. His research interests include information processing, intelligence computation and machine learning.
  • Last Modified: 2016-3-3

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      Projects


    National Natural Science Foundation of China (NSFC):
  • Optimization models for non-parallel hyperplanes support vector machines via structural regularization (No.11426202)
  • Optimization models and algorithms for non-parallel hyperplanes-based support vector machines (No.11201426)
  • Dynamic modeling and performance evaluation of aromatics isomerization reaction system (No.61304125)

    • Zhejiang Provincial Natural Science Foundation of China:
  • Maximum margin and structural information based non-parallel hyperplanes support vector machines (No.LQ13F030010)
  • Non-parallel support vector machine for large-scale data (No.LY15F030013)

    • Ministry of Education, Humanities and SocialSciences Research Project:
  • China financial index and interest rate study based on support vector regression machine (No.13YJC910011)

    • Scientific Research Fund of Zhejiang Provincial Education Department :
  • Optimization models for twin support vector machine and its applications (No.Y201225179)

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      Publications


    {2016} {2015} {2014} {2013} {2012} {2011}

    2016
    1. Wei-Jie Chen,Yuan-Hai Shao, Chun-Na Li, Nai-Yang Deng*. MLTSVM: A novel twin support vector machine to multi-label learning[J]. Pattern Recogn. 2016, 52: 61-74. (SCI, IF:3.096)
    2. Wei-Jie Chen*, Chun-Na Li,Yuan-Hai Shao, Nai-Yang Deng. Semi-supervised projection twin support vector machine via manifold regularization[J]. (In Chinese) Pattern Recogn. & Artif. Intell. 28(2), pp 97-107

    2015
    1. Yuan-Hai Shao, Wei-Jie Chen, Zhen Wang, Chun-Na Li, Nai-Yang Deng*. Weighted linear loss twin support vector machine for large-scale classification[J]. Knowl-Based Syst. 2015, 73:276–288. (SCI, IF:3.058, WOS:000346224900023)
    2. Yuan-Hai Shao, Wei-Jie Chen, Ming-Zeng Liu, Deng, Nai-Yang*. Laplacian unit-hyperplane learning from positive and unlabeled examples[J]. Inform. Sciences. 2015, 314: 152-168. (SCI, IF:3.643, WOS:000355050200010)

    2014
    1. Wei-Jie Chen*, Yuan-Hai Shao*, Nai-Yang Deng. Laplacian least squares twin support vector machine for semi-supervised classification[J]. Neurocomputing. 2014, 145:465-476. (SCI, IF:2.005, WOS:000342248100048)
    2. Yuan-Hai Shao, Wei-Jie Chen, Zhen Wang, Hai-Bin Zhang, Nai-Yang Deng*. A proximal classifier with positive and negative local regions[J]. Neurocomputing. 2014, 145:131-139. (SCI, IF:2.005, WOS:000342248100017)
    3. Wei-Jie Chen*, Yuan-Hai Shao, Ning Hong. Laplacian smooth twin support vector machine for semi-supervised classification[J]. Int. J. Mach. Learn. Cyber. 2014,5(3):459–468 (SCI, WOS:000348040100011)
    4. Wei-Jie Chen*, Yuan-Hai Shao, Deng-Ke Xu and Yong-Feng Fu. Manifold proximal support vector machine for semi-supervised classification[J]. Appl. Intell. 2014,40(4):623-638.(SCI, IF:1.875, WOS:000335656200006)
    5. Yuan-Hai Shao, Wei-Jie Chen, Jing-Jing Zhang, Zhen Wang and Nai-Yang Deng*. An efficient weighted Lagrangian twin support vector machine for imbalanced data classification[J]. Pattern Recogn. 2014,47(9):3158-3167. (SCI, IF:2.632, WOS:000336872000029)
    6. Yuan-Hai Shao, Wei-Jie Chen and Nai-Yang Deng*. Nonparallel hyperplane support vector machine for binary classification problems[J]. Inform. Sciences. 2014,263:22-35.(SCI, IF:3.643, WOS:000331919400002) [Code]

    2013
    1. Wei-Jie Chen*, Yuan-Hai Shao, Ya-Fen Ye. Maximum margin eigenvalue proximal support vector regressor[J]. Control and Decision (In Chinese). 2013,28 (12):1817-1821. (EI JA, 20140317204002)
    2. Yuan-Hai Shao, Nai-Yang Deng*, Wei-Jie Chen. A proximal classifier with consistency[J]. Knowl-Based Syst. 2013,49:171-178. (SCI, IF:3.058, WOS:000322428100016) [Code]
    3. Wei-Jie Chen*, Quan Wang, Yuan-Hai Shao and Zhi-lin Feng. Manifold based twin parametric-margin SVM for semi-supervised classification[J]. Int. J. Comput. Inform. Systems. 2013, 9(23): 9253-9260 (EI JA, 20140517248396)
    4. Wei-Jie Chen*, Yuan-Hai Shao*, Ya-Fen Ye. Improving Lap-TSVM with successive overrelaxation and differential evolution[C]. Procedia Computer Science. 2013,17:33-40. (SCI Proceedings, WOS:000339094400006)
    5. Yuan-Hai Shao*, Wei-Jie Chen, Wen-Biao Huang, Zhi-Min Yang, Nai-Yang Deng*. The Best Separating Decision Tree Twin Support Vector Machine for Multi-Class Classification[C]. Procedia Computer Science. 2013,17:1032-1038. (SCI Proceedings, WOS:000339094400131)
    6. Wei-Jie Chen, Yuan-Hai Shao, Yi-Bo Jiang*, Chong-Pu Xia. Ensemble learning for generalised eigenvalues proximal support vector machines[J]. Int. J. Comput. Appl. Technol. 2013, 47(2/3):273-279. (EI JA, 20132516438882)
    7. Yuan-Hai Shao, Zhen Wang, Wei-Jie Chen, Nai-Yang Deng*. Least squares twin parametric-margin support vector machines for classification[J]. Appl. Intell. 2013, 39(3):451-464. (SCI, IF:1.849, WOS:000324107400001)
    8. Yuan-Hai Shao, Nai-Yang Deng*, Wei-Jie Chen. Improved generalized eigenvalue proximal support vector machine[J]. IEEE Signal Processing Letters. 2013,20(3):213-216. (SCI IF:1.388, WOS:000314723300002)
    9. Ya-Fen Ye*, Yuan-Hai Shao, Wei-Jie Chen. Comparing inflation forecasts using an e-wavelet twin support vector regression[J]. J. Inf. Comput. Sci. 2013,10(7):2041-2049. (EI JA, 20132316396461)
    10. Yuan-Hai Shao, Nai-Yang Deng*, Wei-Jie Chen, Zhen Wang. A regularization for the projection twin support vector machine[J]. Knowledge-Based Systems. 2013,37: 203-210. (SCI, IF:2.41, WOS:000313761800019)
    11. Wei-Jie Chen*, Yuan-Hai Shao, Hai-Yan Wang. Successive overrelaxation for twin parametric-margin support vector machines[J]. J. Inf. Comput. Sci. 2013,10(3):791-799. (EI JA, 20131016089915)
    12. Wei-Jie Chen*, Yuan-Hai Shao, Zhi-Jun Zhu. Fast twin parametric-insensitive SVR using successive over-relaxation[J]. J. Conver. Inf. Technol. 2013,8(1): 681-690. (EI JA, 20130415927786)

    2012
    1. Wei-Jie Chen*, Yuan-Hai Shao, Wei-Bing Bao. A Novel Ensemble TBSVM Classifier for Imbalanced Data classification[J]. J. Comput. Inf. Syst. 2012,8(19):8223-8230. (EI JA, 20124515645121)
    2. Yuan-Hai Shao, Nai-Yang Deng*, Zhi-Ming Yang, Wei-Jie Chen. Probabilistic outputs for twin support vector machines[J]. Knowledge-Based Systems. 2012,33:145–151. (SCI, IF:2.41, WOS:000305719900013)
      3. Yi-Bo Jiang*, Wan-Liang Wang, Wei-Jie Chen. Coverage Optimization of Occlusion-Free Surveillance for Video Sensor Networks[J]. Journal of Software (In Chinese). 2012,32(02):310-322. (EI JA, 20121014842128)

    2011
    1. Wei-Jie Chen, Wan-Liang Wang*, Jian-Wei Zheng, Yi-Bo Jiang. NFL: An Adaptive Fuzzy-logic-based AQM Algorithm with Active-flow Parameter Estimation[J]. Control and Decision (In Chinese). 2011,26(12):1791-1795+1802. (EI JA, 20120214672662)
    2. Wei-Jie Chen, Wan-Liang Wang*, Yi-Bo Jiang, Jian-Wei Zheng. SABlue: A Self-tune AQM Algorithm with Acceleration Factor[J]. Journal of Electronics and Information Technology (In Chinese). 2011,33(02):479-483. (EI JA, 20111413890566)
    3. Wan-liang Wang*, Qing-Li Chen, Wei-Jie Chen, Yi-Bo Jiang. The stability of TCP/REM congestion control mechanism[J]. J. Comput. Inf. Syst. 2011,8(14):2925-2932. (EI JA, 20115214650008)
    4. Xin-Wei Yao, Wan-Liang Wang*, Wei-Jie Chen, Yan-Wei Zhao. Congestion awareness adaptive mapping mechanism oriented to remote video maintenance applications[J]. Computer Integrated Manufacturing Systems (In Chinese). 2011,17(01):216-223. (EI JA, 20111313881878)
    5. Jian-Wei Zheng, Wan-Liang Wang*, Yi-Bo Jiang, Wei-Jie Chen. Probabilistic Sparse Kernel Logistic Multi-classifier[J]. Journal of Electronics and Information Technology (In Chinese). 2011,33(07):1632-1638. (EI JA, 20113514276909)
    6. Wei-Jie Chen, Wan-Liang Wang*, Yi-Bo Jiang. PFL: Proactive Fuzzy-logic-based AQM Algorithm for Best-effort Networks[C]. IEEE CASoN 2010. Taiyuan, China, 2010,2(1):315-318. (EI, 20105213515827)
    7. Yi-Bo Jiang*, Wan-Liang Wang, Wei-Jie Chen. A Coverage Enhancement Method of Directional Sensor Network Based on Genetic Algorithm for Occlusion-Free Surveillance[C]. IEEE CASoN 2010. Taiyuan, China, 2010,2(1):311-314. (EI, 20105213515892)

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      Resources


    {Journal} {Researchers} {Tools}

    Journal
  • IEEE Transactions on Pattern Analysis and Machine Intelligence (IF:4.795)
  • IEEE Transactions on Neural Networks and Learning Systems (IF:3.766)
  • Pattern Recognition (IF:2.632)
  • Information Sciences (IF:3.643)
  • Knowledge-Based Systems (IF:4.104)
  • Neurocomputing (IF:1.634)
  • Applied Intelligence (IF:1.853)
  • Pattern Analysis and Applications (IF:0.814)
  • Neural Computing and Applications (IF:1.168)

    Researchers
  • LAMDA Group (Zhi-Hua Zhou)
  • Min-Ling ZHANG: Multi-Label Learning
  • LABIC Group: Laboratory of Intelligent Computation (Labic) Lead by Jian-hua Xu. The current research interests focus on: multi-label classification, hierarchical classification, multi-objective optimization, bioinformatics and so on.

    Tools
  • Weka 3: Data Mining Software in Java.
  • Mulan: A Java Library for Multi-Label Learning. It's an useful extension of Weka for Multi-label learning.
  • Mldatagen: Synthetic Dataset Generator for Multi-label Learning. (Weka format)
  • scikit-learn: Machine Learning in Python.
  • LIBSVM: A Library for Support Vector Machines.
  • LibCVM Toolkit: The LibCVM Toolkit is a C++ implementation of the improved Core Vector Machine (CVM) and recently developed Ball Vector Machine (BVM), which are fast Support Vector Machine (SVM) training algorithms using core-set approximation on very large scale data sets.
  • Orange: Open source data visualization and analysis for novice and experts. Data mining through visual programming or Python scripting.
  • AlphaMiner: It is an open source data mining platform that provides the best cost-and-performance ratio for data mining applications.

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