Username   Password       Forgot your password?  Forgot your username? 


Piecewise Combination of Hyper-Sphere Support Vector Machine for Multi-Class Classification Problems

Volume 15, Number 6, June 2019, pp. 1611-1619
DOI: 10.23940/ijpe.19.06.p12.16111619

Shuang Liua, Peng Chenb, Jiayi Lia, Hui Yanga, and Niko Lukačc

aSchool of Computer Science and Engineering, Dalian Minzu University, Dalian, 116600, China
bDepartment of Software Engineering, Dalian Neusoft University of Information, Dalian, 116023, China
cFaculty of Electrical Engineering and Computer Science, University of Maribor, Maribor, SI-2000, Slovenia


(Submitted on March 20, 2019; Revised on April 4, 2019; Accepted on June 8, 201)


Hyper-sphere Support vector machine (SVM) is a widely used machine learning method for multi-class classification problems such as image recognition, text classification, or handwriting recognition. In most cases, only one hyper-sphere optimization problem is computed to solve the problem. However, there are many complex applications with complicated data distributions. In these cases, the computation cost will be increased with unsatisfied classification results if only one support vector machine is adopted as the classification decision rule. To achieve good classification performance, a piecewise combination of the hyper-sphere support vector machine is put forward in this paper based on the analysis of the data sample distribution. First, statistical analysis is adopted for the original data. Then, the k-means cluster algorithm is introduced to compute cluster centers for different classes of the data. For the n classes classification problem, m (m > n) hyper-spheres are computed to solve the objective problems based on the number of data centers. For simple sphere-distribution and locally linearly separable distribution cases, the minimum enclosing and maximum excluding support vector machine and the combination of hyper-sphere support vector machine are defined. Experimental results show that different support vector machines for different data distributions will improve the final classification performance.


References: 22

  1. V. N. Vapnik, “An Overview of Statistical Learning Theory,” IEEE Transactions on Neural Networks, Vol. 10, No. 5, pp. 988-999, 1999
  2. V. N. Vapnik, “The Nature of Statistical Learning Theory,” Springer-Verlag, New York, 1999
  3. J. Weston and C. Watkins, “Support Vector Machines for Multi-Class Pattern Recognition,” in Proceedings of the European Symposium on Artificial Neural Networks, pp. 219-224, Bruges, 1999
  4. C. W. Hsu and C. J. Lin, “A Comparison of Methods for Multi-Class Support Vector Machines,” IEEE Transactions on Neural Networks, Vol. 13, No. 2, pp. 415-425, 2002
  5. M. L. Zhu, S. F. Chen, and X. D. Liu, “Sphere-Structured Support Vector Machines for Multi-Class Pattern Recognition,” Lecture Notes in Computing Science, Vol. 2369, pp. 589-593, 2003
  6. Q. Wu, C. Y. Jia, and A. F. Zhang, “An Improved Algorithm based on Sphere Structure SVMs and Simulation,” Journal of System Simulation (Chinese), Vol. 20, No. 2, pp. 345-348, 2008
  7. Y. Chen, X. S. Zhou, and T. S. Huang, “One-Class SVM for Learning in Image Retrieval,” in Proceedings of the 2001 IEEE International Conference On Image Processing, pp. 34-37, 2001
  8. V. Vural and J. G. Dy, “A Hierarchical Method for Multi-Class Support Vector Machines,” in Proceedings of International Conference on Machine Learning, pp. 831-838, ACM, 2004
  9. K. Mele and J. Maver, “Object Recognition using Hierarchical SVMs,” in Proceedings of Computer Vision Winter Workshop '03, Valtice, Czech Republic, 2003
  10. K. Benabdeslem and Y. Bennani, “Dendogram-based SVM for Multi-Class Classification,” Journal of Computing & Information Technology, Vol. 14, No. 4, pp. 283-289, 2006
  11. W. Chmielnicki and K. Sta̧por, “Combining One-Versus-One and One-Versus-All Strategies to Improve Multiclass SVM Classifier,” in Proceedings of the 9th International Conference on Computer Recognition Systems, pp. 37-45, 2016
  12. T. Le, D. Tran, W. Ma, and D. Sharma, “A Theoretical Framework for Multi-Sphere Support Vector Data Description,” in Proceedings of the 17th International Conference on N Neural Information Processing Models & Applications ICONIP'10, Vol. part II, pp. 132-142 , Sydney, Australia, 2010
  13. H. Cevikalp and B. Triggs, “Visual Object Detection using Cascades of Binary and One-Class Classifiers,” International Journal of Computer Vision, Vol. 123, No. 3, pp. 334-349, 2017
  14. Y. S. Xiao, B. Liu, L. B. Cao, X. D. Wu, C. Q. Zhang, Z. F. Hao, et al., “Multi-Sphere Support Vector Data Description for Outliers Detection on Multi-Distribution Data,” in Proceedings of 2009 IEEE International Conference on Data Mining Workshops, pp. 82-87, 2009
  15. Y. Liu and Y. F. Zheng, “Minimum Enclosing and Maximum Excluding Machine for Pattern Description and Discrimination,” in Proceedings of the 18th International Conference on Pattern Recognition, Vol. 3, pp. 129-132, 2006
  16. Z. Yang, N. Meratnia, and P. Havinga, “An Online Outlier Detection Technique for Wireless Sensor Networks using Unsupervised Quarter-Sphere Support Vector Machine,” in Proceedings of International Conference on Intelligent Sensors, pp. 151-156, 2008
  17. S. Liu, P. Chen, and K. Q. Li, “Multiple Sub-Hyper-Spheres Support Vector Machine for Multi-Class Classification,” International Journal of Wavelets Multiresolution and Information Processing, Vol. 12, No. 3, 2014
  18. S. Liu, P. Chen, and J. Yun, “Fuzzy Hyper-Sphere Support Vector Machine for Pattern Recognition,” ICIC Express Letters, Vol. 9, No. 1, pp. 87-92, 2015
  19. A. Rabaoui, M. Davy, S. Rossignol, Z. Lachiri, and N. Ellouze, “Improved One-Class SVM Classifier for Sounds Classification,” in Proceedings of IEEE Conference on Advanced Video & Signal based Surveillance, pp. 117-122, 2007
  20. J. Munoz-Mari, F. Bovolo, L. Gomez-Chova, L. Bruzzone, and G. Camp-Valls, “Semisupervised One-Class Support Vector Machines for Classification of Remote Sensing Data,”  IEEE Transactions on Geoscience and Remote Sensing, Vol. 48, No. 8, pp. 3188-3197, 2010
  21. S. Mohanty, “Speaker Identification using SVM During Oriya Speech Recognition,” I. J. Image, Graphics and Signal Processing, Vol. 10, pp. 28-36, 2015
  22. A. Sun, E. P. Lim, and Y. Liu, “On Strategies for Imbalanced Text Classification using SVM: A Comparative Study,” Decision Support Systems, Vol. 48, No. 1, pp. 191-201, 2009


Please note : You will need Adobe Acrobat viewer to view the full articles.Get Free Adobe Reader

This site uses encryption for transmitting your passwords.