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Robust Parametric Twin Support Vector Machine for Pattern Classification

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Abstract

In this paper, we propose a robust parametric twin support vector machine (RPTWSVM) classifier based on Parametric-\(\nu \)-Support Vector Machine (Par-\(\nu \)-SVM) and twin support vector machine. In order to capture heteroscedastic noise present in the training data, RPTWSVM finds a pair of parametric margin hyperplanes that automatically adjusts the parametric insensitive margin to incorporate the structural information of data. The proposed model of RPTWSVM is not only useful in controlling the heteroscedastic noise but also has much faster training speed when compared to Par-\(\nu \)-SVM. Experimental results on several machine learning benchmark datasets show the advantages of RPTWSVM both in terms of generalization ability and training speed over other related models.

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References

  1. Asuncion A, Newman DJ (2007) UCI machine learning repository. University of California, Irvine, CA. School of Information and Computer Science, 12. http://www.ics.uci.edu/~mlearn/MLRepository.html

  2. Cao L, Tay FE (2001) Financial forecasting using support vector machines. Neural Comput Appl 10(2):184–192

    Article  MATH  Google Scholar 

  3. de Carvalho ACPLF, Freitas AA (2009) A tutorial on multi-label classification techniques. In: Abraham A, Hassanien A-E, Snášel V (eds) Foundations of computational intelligence, vol 5. Springer, Berlin, pp 177–195

  4. Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297

    MATH  Google Scholar 

  5. Debnath R, Takahide N, Takahashi H (2004) A decision based one-against-one method for multi-class support vector machine. Pattern Anal Appl 7(2):164–175. doi:10.1007/s10044-004-0213-6

    Article  MathSciNet  Google Scholar 

  6. Demšar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7:1–30

    MathSciNet  MATH  Google Scholar 

  7. Ding S, Huang H, Xu X, Wang J (2014) Polynomial smooth twin support vector machines. Appl Math Inf Sci 8(4):2063

    Article  MathSciNet  Google Scholar 

  8. Ding S, Yu J, Qi B, Huang H (2014) An overview on twin support vector machines. Artif Intell Rev 42(2):245–252

    Article  Google Scholar 

  9. Duda RO, Hart PE, Stork DG (2000) Pattern classification. Wiley, London

    MATH  Google Scholar 

  10. Famoye F, Johnson NL, Kotz S, Balakrishnan N (1995) Continuous univariate distributions, volume 1. Technometrics 37(4):466

  11. Golub GH, Van Loan CF (2012) Matrix computations, vol 3. JHU Press, Baltimore

    MATH  Google Scholar 

  12. Hao PY (2010) New support vector algorithms with parametric insensitive/margin model. Neural Netw 23(1):60–73

    Article  Google Scholar 

  13. Holm S (1979) A simple sequentially rejective multiple test procedure. Scand J Stat 6:65–70

  14. Hsu C-W, Chang C-C, Lin C-J et al (2003) A practical guide to support vector classification, pp 1–16. www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf

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

    Article  MATH  Google Scholar 

  16. Joachims T (1998) Text categorization with support vector machines: learning with many relevant features. In: Nédellec C, Rouveirol C (eds) Machine learning: ECML-98. ECML 1998. Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence), vol 1398. Springer, Berlin, pp 137–142

  17. Khemchandani R, Saigal P (2015) Color image classification and retrieval through ternary decision structure based multi-category twsvm. Neurocomputing 165:444–455

    Article  Google Scholar 

  18. Khemchandani R, Sharma S (2016) Robust least squares twin support vector machine for human activity recognition. Appl Soft Comput 47:33–46

    Article  Google Scholar 

  19. Kreßel UH-G (1999) Pairwise classification and support vector machines. In: Schölkopf B, Burges C, Smola A (eds) Advances in kernel methods: support vector learning. MIT Press, Cambridge, pp 255–268

  20. Lei H, Govindaraju V (2005) Half-against-half multi-class support vector machines. In: International workshop on multiple classifier systems. Springer, pp 156–164

  21. Manosha Chathuramali K, Rodrigo R (2012) Faster human activity recognition with svm. In: 2012 International Conference on advances in ICT for emerging regions (ICTer). IEEE, pp 197–203

  22. Milgram J, Cheriet M, Sabourin R (2006) One against one or one against all: which one is better for handwriting recognition with svms? In: Tenth international workshop on frontiers in handwriting recognition. Suvisoft

  23. Musicant D (1998) Ndc: normally distributed clustered datasets. Computer Sciences Department, University of Wisconsin, Madison

    Google Scholar 

  24. Peng X (2010) A \(\nu \)-twin support vector machine (\(\nu \)-tsvm) classifier and its geometric algorithms. Inf Sci 180(20):3863–3875

    Article  MathSciNet  MATH  Google Scholar 

  25. Peng X (2011) TPMSVM: a novel twin parametric-margin support vector machine for pattern recognition. Pattern Recognit 44(10–11):2678–2692

    Article  MATH  Google Scholar 

  26. Schlkopf B, Smola AJ, Williamson RC, Bartlett PL (2000) New support vector algorithms. Neural Comput 12(5):1207–1245

    Article  Google Scholar 

  27. Schölkopf B, Tsuda K, Vert JP (2004) Kernel methods in computational biology. MIT press, Cambridge

    Google Scholar 

  28. Shao YH, Chen WJ, Huang WB, Yang ZM, Deng NY (2013) The best separating decision tree twin support vector machine for multi-class classification. Procedia Comput Sci 17:1032–1038

    Article  Google Scholar 

  29. Shao YH, Wang Z, Chen WJ, Deng NY (2013) Least squares twin parametric-margin support vector machine for classification. Appl Intell 39(3):451–464

    Article  Google Scholar 

  30. Simes RJ (1986) An improved bonferroni procedure for multiple tests of significance. Biometrika 73(3):751–754

    Article  MathSciNet  MATH  Google Scholar 

  31. Tian Y, Ju X, Qi Z, Shi Y (2014) Improved twin support vector machine. Sci China Math 57(2):417–432

    Article  MathSciNet  MATH  Google Scholar 

  32. Tomar D, Agarwal S (2015) A comparison on multi-class classification methods based on least squares twin support vector machine. Knowl Based Syst 81:131–147

    Article  Google Scholar 

  33. Vapnik VN (1995) The nature of statistical learning theory. Springer, Berlin

    Book  MATH  Google Scholar 

  34. Wang Z, Shao YH, Wu TR (2014) Proximal parametric-margin support vector classifier and its applications. Neural Comput Appl 24(3–4):755–764

    Article  Google Scholar 

  35. Xu Y, Yang Z, Pan X (2017) A novel twin support-vector machine with pinball loss. IEEE Trans Neural Netw Learn Syst 28(2), 359–370

  36. Yang Z, Xu Y (2016) Laplacian twin parametric-margin support vector machine for semi-supervised classification. Neurocomputing 171:325–334

    Article  Google Scholar 

  37. Yang ZM, Hua XY, Shao YH, Ye YF (2016) A novel parametric-insensitive nonparallel support vector machine for regression. Neurocomputing 171:649–663

    Article  Google Scholar 

  38. Yang ZX, Shao YH, Zhang XS (2013) Multiple birth support vector machine for multi-class classification. Neural Comput Appl 22(1):153–161

    Article  Google Scholar 

  39. Zhang X, Ding S, Sun T (2016) Multi-class lstmsvm based on optimal directed acyclic graph and shuffled frog leaping algorithm. Int J Mach Learn Cybern 7(2):241–251

    Article  Google Scholar 

  40. Zhang X, Ding S, Xue Y (2017) An improved multiple birth support vector machine for pattern classification. Neurocomputing 225:119–128

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank the editor and the anonymous reviewers whose valuable comments and feedback have helped us to improve the content and presentation of the paper.

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Authors and Affiliations

  1. Department of Computer Science, Faculty of Mathematics and Computer Science, South Asian University, New Delhi, India

    Reshma Rastogi & Sweta Sharma

  2. Department of Mathematics, Indian Institute of Technology, Delhi, New Delhi, India

    Suresh Chandra

Authors
  1. Reshma Rastogi
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  2. Sweta Sharma
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  3. Suresh Chandra
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Correspondence to Reshma Rastogi.

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Rastogi, R., Sharma, S. & Chandra, S. Robust Parametric Twin Support Vector Machine for Pattern Classification. Neural Process Lett 47, 293–323 (2018). https://doi.org/10.1007/s11063-017-9633-3

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  • DOI: https://doi.org/10.1007/s11063-017-9633-3

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