Skip to main content
SpringerLink
Log in

A Novel Twin Support Vector Machine for Binary Classification Problems

  • Published:
Neural Processing Letters Aims and scope Submit manuscript

Abstract

Based on the recently proposed twin support vector machine and twin bounded support vector machine, in this paper, we propose a novel twin support vector machine (NTSVM) for binary classification problems. The significance of our proposed NTSVM is that the objective function is changed in the spirit of regression, such that hyperplanes separate as much as possible. In addition, the successive overrelaxation technique is used to solve quadratic programming problems to speed up the training process. Experimental results obtained on several artificial and UCI benchmark datasets show the feasibility and effectiveness of the proposed method.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Turk M, Pentland A (1991) Eigenfaces for recognition. J Cognit Neurosci 3(1):71–86

    Article  Google Scholar 

  2. Belhumeur PN, Hespanha JP, Kriegman DJ (1997) Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE Trans Pattern Anal Mach Intell 19(7):711–720

    Article  Google Scholar 

  3. Yang J, Zhang D, Frangi AF, Yang JY (2004) Two-dimensional PCA: a new approach to face representation and recognition. IEEE Trans Pattern Anal Mach Intell 26(1):131–137

    Article  Google Scholar 

  4. Tao DC, Li XL, Hu WM et al (2005) Supervised tensor learning. In: Fifth IEEE international conference on data mining

  5. Tao DC, Li XL, Wu XD, Maybank SJ (2007) General tensor discriminant analysis and gabor features for gait recognition. IEEE Trans Pattern Anal Mach Intell 29(10):1700–1715

    Article  Google Scholar 

  6. Hong CQ, Yu J, Li J, Chen XH (2013) Multi-view hypergraph learning by patch alignment framework. Neurocomputing 118:79–86

    Article  Google Scholar 

  7. Yu J, Rui Y, Tang YY, Tao DC (2014) High-order distance-based multiview stochastic learning in image classification. IEEE Trans Cybern 44(12):2431–2442

    Article  Google Scholar 

  8. Wang Z, Chen SC, Sun TK (2008) MultiK-MHKS: a novel multiple kernel learning algorithm. IEEE Trans Pattern Anal Mach Intell 30(2):348–353

    Article  Google Scholar 

  9. Pong KH, Lam KM (2014) Multi-resolution feature fusion for face recognition. Pattern Recognit 47(2):556–567

    Article  Google Scholar 

  10. Cortes C, Vapnik VN (1995) Support vector machine. Mach Learn 20(3):273–297

    MATH  Google Scholar 

  11. Vapnik VN (2000) The nature of statistical learning theory. Springer, New York

    Book  MATH  Google Scholar 

  12. Byun H, Lee SW (2002) Applications of support vector machines for pattern recognition: a survey, pattern recognition with support vector machines. Springer, Berlin

    Book  MATH  Google Scholar 

  13. Burges C (1998) A tutorial support vector machines for pattern recognition. Data Mining Knowl Discov 2:1–43

    Article  Google Scholar 

  14. Noble WS (2004) Kernel methods in computational biology. In: Vert J-P (ed) Support vector machine applications in computational biology. MIT Press, Cambridge, pp 71–92

    Google Scholar 

  15. Isa D, Lee LH, Kallimani VP, Rajkumar R (2008) Text document preprocessing with the Bayes formula for classification using the support vector machine. IEEE Trans Knowl Data Eng 20(9):1264–1272

    Article  Google Scholar 

  16. Yen SJ, Wu YC, Yang JC, Lee YS, Liu LL (2013) A support vector machine-based context-ranking model for question answering. Inf Sci 224(1):77–87

    Article  Google Scholar 

  17. Trafails TB, Ince H (2000) Support vector machine for regression and applications to financial forecasting. In: International joint conference on neural networks, pp 6348–6348

  18. Tao DC, Tang XO, Li XL, Wu XD (2006) Asymmetric bagging and random subspace for support vector machines-based relevance feedback in image retrieval. IEEE Trans Pattern Anal Mach Intell 28(7):1088–1099

    Article  Google Scholar 

  19. Liu WF, Tao DC (2013) Multiview Hessian regularization for image annotation. IEEE Trans Image Process 22(7):2676–2687

    Article  MathSciNet  Google Scholar 

  20. Tao DC, Li XL, Wu XD, Maybank SJ (2009) Geometric mean for subspace selection. IEEE Trans Pattern Anal Mach Intell 31(2):260–274

    Article  Google Scholar 

  21. Mangasarian OL, Wild EW (2006) Multisurface proximal support vector machine classification via generalized eigenvalues. IEEE Trans Pattern Anal Mach Intell 28(1):69–74

    Article  Google Scholar 

  22. Jayadeva R, Khemchandani S Chandra (2007) Twin support vector machines for pattern classification. IEEE Trans Pattern Anal Mach Intell 29(5):905–910

    Article  MATH  Google Scholar 

  23. Kumar MA, Gopal M (2008) Application of smoothing technique in twin support vector machines. Pattern Recognit Lett 29(13):1842–1848

    Article  Google Scholar 

  24. Shao YH, Zhang CH, Wang XB, Deng NY (2011) Improvements on twin support vector machines. IEEE Trans Neural Netw 22(6):962–968

    Article  Google Scholar 

  25. Peng XJ (2011) TPMSVM: a novel twin parametric-margin support vector machine for pattern recognition. Pattern Recognit 44(10):2678–2692

    Article  MATH  Google Scholar 

  26. Qi ZQ, Tian YJ, Shi Y (2013) Robust twin support vector machine for pattern classification. Pattern Recognit 46(1):305–316

    Article  MATH  Google Scholar 

  27. Qi ZQ, Tian YJ, Shi Y (2013) Structural twin support vector machine for classification. Knowl-Based Syst 43:74–81

    Article  Google Scholar 

  28. Wang Z, Shao YH, Wu TR (2014) Proximal parametric-margin support vector classifier and its applications. Neural Comput Appl 24:755–764

    Article  Google Scholar 

  29. Shao YH, Chen WJ, Deng NY (2014) Nonparallel hyperplane support vector machine for binary classification problems. Inf Sci 263:22–35

    Article  MathSciNet  MATH  Google Scholar 

  30. Ye QL, Zhao CX, Ye N, Chen YN (2010) Multi-weight vector projection support vector machines. Pattern Recognit Lett 31:2006–2011

    Article  Google Scholar 

  31. Chen XB, Yang J, Ye QL, Liang J (2011) Recursive projection twin support vector machine via within-class variance minimization. Pattern Recognit 44(10):2643–2655

    Article  MATH  Google Scholar 

  32. Shao YH, Deng NY, Yang ZM (2012) Least squares recursive projection twin support vector machine for classification. Pattern Recognit 45(6):2299–2307

    Article  MATH  Google Scholar 

  33. Shao YH, Wang Z, Chen WJ, Deng NY (2013) A regularization for the projection twin support vector machine. Knowl-Based Syst 37:203–210

    Article  Google Scholar 

  34. Ding SF, Hua XP (2014) Recursive least squares projection twin support vector machines for nonlinear classification. Neurocomputing 130:3–9

    Article  Google Scholar 

  35. Peng XJ (2010) Least squares twin support vector hypersphere (LS-TSVH) for pattern recognition. Expert Syst Appl 37(12):8371–8378

    Article  Google Scholar 

  36. Peng XJ, Xu D (2014) Twin support vector hypersphere (TSVH) classifier for pattern recognition. Neural Comput Appl 24:1207–1220

    Article  MathSciNet  Google Scholar 

  37. Fung G, Mangasarian OL (2001) Proximal support vector machine classifiers. In: Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, pp 77–86

  38. Mangasarian OL, Musicant DR (1999) Successive overrelaxation for support vector machines. IEEE Trans Neural Netw 10(5):1032–1037

    Article  Google Scholar 

  39. Lee YJ, Huang SY (2007) Reduced support vector machines: a statistical theory. IEEE Trans Neural Netw 13(1):1–13

    Article  Google Scholar 

  40. Duda RO, Hart PE, Stork DG (2001) Pattern classification, 2nd edn. Wiley, New York

    MATH  Google Scholar 

  41. Ripley BD (2008) Pattern recognition and neural networks. Cambridge University Press, Cambridge

    MATH  Google Scholar 

  42. Muphy PM, Aha DW (1992) UCI repository of machine learning databases

  43. Musicant DR (1998) NDC: normally distributed clustered datasets. http://www.cs.wisc.edu/dmi/svm/ndc/. Accessed 5 Nov 2015

  44. Xie XJ, Sun SL (2014) Multi-view Laplacian twin support vector machines. Appl Intell 41(4):1059–1068

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgments

This work is supported in part by the National Natural Science Foundation of China under grant No. 61373055 and No. 61103128. The authors would like to thank Dr. Yuan-Hai Shao from Zhejiang University of Technology for his valuable discussion and help.

Author information

Authors and Affiliations

  1. School of IoT Engineering, Jiangnan University, Wuxi, 214122, People’s Republic of China

    Sugen Chen, Xiaojun Wu & Renfeng Zhang

  2. School of Mathematics & Computational Science, Anqing Normal University, Anqing, 246133, People’s Republic of China

    Sugen Chen

Authors
  1. Sugen Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiaojun Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Renfeng Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xiaojun Wu.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, S., Wu, X. & Zhang, R. A Novel Twin Support Vector Machine for Binary Classification Problems. Neural Process Lett 44, 795–811 (2016). https://doi.org/10.1007/s11063-016-9495-0

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11063-016-9495-0

Keywords

Search

Navigation