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Twin support vector machine based on adjustable large margin distribution for pattern classification

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Abstract

This paper researches the value of the margin distribution in binary classifier. The central idea of large margin distribution machine (LDM) is to optimize the margin distribution, such as maximizing the margin mean and minimizing the margin variance. Compared to support vector machine (SVM), LDM demonstrates the good generalization performance. In order to improve the generalization performance of twin support vector machine (TSVM), a twin support vector machine based on adjustable large margin distribution (ALD-TSVM) is proposed in this paper. Firstly, the margin distribution is redefined to construct a pair of adjustable supporting hyperplanes. Then, the redefined margin distribution is introduced onto TSVM to obtain the models of ALD-TSVM, including linear case and nonlinear case. ALD-TSVM is a general learning method which can be used in any place where TSVM and LDM can be applied. Finally, the novel method is compared with other classification algorithms by doing experiments on toy dataset, UCI datasets and image datasets. The experimental results show that ALD-TSVM obtains better classification performance.

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References

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

    MATH  Google Scholar 

  2. Vapnik VN (2000) The nature of statistical learning theory. Translated by Zhang Xuegong. Tsinghua University Press, Beijing

    Google Scholar 

  3. Richhariya B, Tanveer M (2018) EEG signal classification using universum support vector machine. Expert Syst Appl 106:169–182

    Google Scholar 

  4. Fan Q, Wang Z, Li D, Gao D, Zha H (2017) Entropy-based fuzzy support vector machine for imbalanced datasets. Knowl Based Syst 115:87–99

    Google Scholar 

  5. Wei J, Jian QZ, Xiang Z (2011) Face recognition method based on support vector machine and particle swarm optimization. Expert Syst Appl 38(4):4390–4393

    Google Scholar 

  6. Fung GM, Mangasarian OL (2005) Multicategory proximal support vector machine classifiers. Mach Learn 59(1–2):77–97

    MATH  Google Scholar 

  7. Huang X, Shi L, Suykens JAK (2014) Support vector machine classifier with pinball loss. IEEE Trans Pattern Anal Mach Intell 36(5):984–997

    Google Scholar 

  8. Lu X, Dong F, Liu X, Chang X (2018) Varying coefficient support vector machines. Stat Probabil Lett 132:107–115

    MathSciNet  MATH  Google Scholar 

  9. Wang D, Zhang X, Fan M, Ye X (2016) Hierarchical mixing linear support vector machines for nonlinear classification. Pattern Recognit 59:255–267

    MATH  Google Scholar 

  10. Lin CF, Wang SD (2002) Fuzzy support vector machines. IEEE Trans Neural Netw 13(2):464–471

    Google Scholar 

  11. Khemchandani R, Karpatne A, Chandra S (2013) Proximal support tensor machines. Int J Mach Learn Cybern 4(6):703–712

    Google Scholar 

  12. Loosli G, Canu S, Ong CS (2016) Learning SVM in Krein spaces. IEEE Trans Pattern Anal Mach Intell 38(6):1204–1216

    Google Scholar 

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

    MATH  Google Scholar 

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

    Google Scholar 

  15. Yang HY, Wang XY, Niu PP, Liu YC (2014) Image denoising using nonsubsampled shearlet transform and twin support vector machines. Neural Netw 57:152–165

    Google Scholar 

  16. Ding M, Yang D, Li X (2013) Fault diagnosis for wireless sensor by twin support vector machine. Math Probl Eng 2013

  17. Chu M, Gong R, Gao S, Zhao J (2017) Steel surface defects recognition based on multi-type statistical features and enhanced twin support vector machine. Chemometr Intell Lab 171:140–150

    Google Scholar 

  18. Kumar MA, Gopal M (2009) Least squares twin support vector machines for pattern classification. Expert Syst Appl 36(4):7535–7543

    Google Scholar 

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

    Google Scholar 

  20. Ding S, Zhang X, An Y, Xue Y (2017) Weighted linear loss multiple birth support vector machine based on information granulation for multi-class classification. Pattern Recognit 67:32–46

    Google Scholar 

  21. Tian Y, Qi Z, Ju X, Liu X (2014) Nonparallel support vector machines for pattern classification. IEEE Trans Cybern 44(7):1067–1079

    Google Scholar 

  22. 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

    MathSciNet  Google Scholar 

  23. Peng X, Xu D (2013) A twin-hypersphere support vector machine classifier and the fast learning algorithm. Inf Sci 221:12–27

    MathSciNet  MATH  Google Scholar 

  24. Freund Y, Schapire RE (1995) A decision-theoretic generalization of on-line learning and an application to boosting. In: Proceedings of the 2nd European conference on computational learning theory, pp 23–37

  25. Breiman L (1999) Prediction games and arcing classifiers. Neural Comput 11(7):1493–1517

    Google Scholar 

  26. Reyzin L, Schapire RE (2006) How boosting the margin can also boost classifier complexity. In: Proceedings of the 23rd international conference on Machine learning, ACM, pp 753–760

  27. Gao W, Zhou Z (2013) On the doubt about margin explanation of boosting. Artif Intell 203(5):1–18

    MathSciNet  MATH  Google Scholar 

  28. Zhang T, Zhou ZH (2014) Large margin distribution machine. In: Proceedings of ACM SIGKDD international conference on knowledge discovery and data mining, ACM, vol 20, pp 313–322

  29. Zhou YH, Zhou ZH (2016) Large margin distribution learning with cost interval and unlabeled data. IEEE Trans Knowl Data Eng 28(7):1749–1763

    Google Scholar 

  30. Cheng F, Zhang J, Li Z, Tang M (2017) Double distribution support vector machine. Pattern Recognit Lett 88:20–25

    Google Scholar 

  31. Abe S (2017) Unconstrained large margin distribution machines. Pattern Recognit Lett 98:96–102

    Google Scholar 

  32. Cheng F, Zhang J, Wen C, Liu Z, Li Z (2017) Large cost-sensitive margin distribution machine for imbalanced data classification. Neurocomputing 224:45–57

    Google Scholar 

  33. Deng N, Tian Y, Zhang C (2012) Support vector machines optimization based theory, algorithms and extensions. Chapman and Hall//CRC, London

    MATH  Google Scholar 

  34. Cheng HX, Wang J (2016) A novel twin large margin distribution machine. Control Decis 31(5):949–954

    MathSciNet  Google Scholar 

  35. Qing W, Qi SW, Sun KY (2017) Weighted least squares twin large margin distribution machine. In: Proceedings of the 2017 VI international conference on network, communication and computing. ACM, pp 253–257

  36. Xu H, McCane B, Szymanski L (2018) Twin bounded large margin distribution machine. Australasian joint conference on artificial intelligence. Springer, Cham, pp 718–729

    Google Scholar 

  37. Dua D, Taniskidou EK. UCI machine learning repository. [Online]. Available: http://archive.ics.uci.edu/ml/

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

    MathSciNet  MATH  Google Scholar 

  39. Nemenyi P (1963) Distribution-free multiple comparisons. https://books.google.fi/books?id=nhDMtgAACAAJ

  40. Pan X, Luo Y, Xu Y (2015) K-nearest neighbor based structural twin support vector machine. Knowl Based Syst 88:34–44

    Google Scholar 

  41. USPS Digit Dataset. [online]. Available: https://www.csie.ntu.edu.tw/

  42. Song K, Yan Y (2013) A noise robust method based on completed local binary patterns for hot-rolled steel strip surface defects. Appl Surf Sci 285:858–864

    Google Scholar 

  43. Everingham M, Van ~ Gool L, Williams CKI, Winn J, Zisserman A (2012) The PASCAL visual object classes challenge 2012 (VOC2012) Results; 2012 [cited 2012 February]. Available: http://www.pascalnetwork.org/challenges/VOC/voc2012/workshop/index.html

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Acknowledgements

This work was supported by Liaoning Province Natural Fund Project (20180550067), Liaoning Province PhD Start-up Fund (201601291), Liaoning Province Ministry of Education Scientific Study Project (2017LNQN11 and 2016TSPY13), University of Science and Technology Liaoning Talent Project Grants (601011507-20) and University of Science and Technology Liaoning Team Building Grants (601013360-17).

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Authors and Affiliations

  1. School of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan, 114051, Liaoning, China

    Liming Liu, Maoxiang Chu, Yonghui Yang & Rongfen Gong

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  1. Liming Liu
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  2. Maoxiang Chu
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  3. Yonghui Yang
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  4. Rongfen Gong
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Correspondence to Maoxiang Chu.

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Liu, L., Chu, M., Yang, Y. et al. Twin support vector machine based on adjustable large margin distribution for pattern classification. Int. J. Mach. Learn. & Cyber. 11, 2371–2389 (2020). https://doi.org/10.1007/s13042-020-01124-4

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  • DOI: https://doi.org/10.1007/s13042-020-01124-4

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