Username   Password       Forgot your password?  Forgot your username? 


Classification Decision based on a Hybrid Method of Weighted kNN and Hyper-Sphere SVM

Volume 14, Number 5, May 2018, pp. 985-994
DOI: 10.23940/ijpe.18.05.p17.985994

Peng Chena,b, Guoyou Shia, Shuang Liuc, Yuanqiang Zhanga, and Denis Špeličd

aNavigation College, Dalian Maritime University, Dalian, 116026, China
bDepartment of Software Engineering, Dalian Neusoft University of Information, Dalian, 116030, China
cSchool of Computer Science & Engineering, Dalian Minzu University, Dalian, 116605, China
dFaculty of Electrical Engineering and Computer Science, University of Maribor, Maribor, SI-2000, Slovenia

(Submitted on February 8, 2018; Revised on March 12, 2018; Accepted on April 23, 2018)


Hyper-sphere Support Vector Machine (SVM) is very effective for solving multi-class classification problems. Considering data distribution is very important for convergence of solving support vectors, a weight factor is imported into the original hyper-sphere SVM. After computing data for each training class, this weight factor is decided by its center-distance ratio. In the training process, data with bigger weight is put into the data processing thread first and is then followed by smaller ones. To save computation cost, a parallel genetic algorithm based SMO multi-threading is adopted. For a test sample, its class decision is based on its position with each classification of hyper-sphere. If all class-specific hyper-spheres are independent of each other, a new test sample can be classified correctly. But, if some hyper-spheres have common spaces, that is, one hyper-sphere intersects with one or more hyper-spheres, it is hard to decide the class of the test sample. Based on detailed analysis of three decision rules for the intersection data classification, one decision rule that combines the kNN method is put forward in this paper. For other simple inclusion cases, the simple decision rule is defined. Through two real experimental results of navigation tracking and ship meeting situations classification, our new proposed algorithm has a higher classification accuracy and boasts a lower computation cost than other algorithms.


References: 11

  1.  P. Chen, S. Liu, "Research on Multiple Sub-hyper-sphere Support Vector Machine," Microelectronics Computer, vol.31, no.12, pp.28-33, 201
  2. S. F. Ding, F. L. Wu, Z. Z. Shi, "Wavelet Twin Support Vector Machine," Neural Computing and Applications, vol.25, no.6, pp. 1241-1247, 2014
  3. R. Kohavi, P. Langley, Y. Yun, "The Utility of Feature Weighting in Nearest-Neighbor Algorithms," Proceedings of the Ninth European Conference on Machine Learning, Springer-Verlag, pp. 85-92, 1997
  4. A. Chittora, O. Mishra, "Face Recognition Using RBF Kernel Based Support Vector Machine," International Journal of Future Computer and Communication, vol.1, no.3, pp.280-283, 2012
  5. S. Liu, P. Chen, 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, 1450035, 2014
  6. 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
  7. John C. Platt, "Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines," Technical Report MSR-TR-98-14, 1998.
  8. G. Y. Shi, S. Liu, "Model Selection of C-Support Vector Machines Based on Multi-threading Genetic Algorithm," International Journal of Wavelets, Multiresolution and Information Processing, vol.11, no.5, 1350041, 2013
  9. Q. Wu, C. Y. Jia and A. F. Zhang, "An Improved Algorithm based on Sphere Structure SVMs and Simulation," Journal of System Simulation, 2008, vol.20, no.2, pp. 345–348.
  10. Y. K. Xu, L. Qin, G. R. Li, etc. "Online Discriminative Structured Output SVM Learning for Multi-target Tracking," IEEE Signal Processing Letters, vol.21, no.2, pp. 190-194, 2014
  11. 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


    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.