Skip to main content
SpringerLink
Log in
Book cover

International Conference on Intelligent Computing

ICIC 2020: Intelligent Computing Methodologies pp 420–431Cite as

Multi-core Twin Support Vector Machines Based on Binary PSO Optimization

  • Conference paper
  • First Online:

  • 1111 Accesses

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12465))

Abstract

How to select the suitable parameters and kernel model is a very important problem for Twin Support vector Machines (TWSVM). In order to solve this problem, one solving algorithm called binary PSO for optimizing the parameters of multi-core Twin Support Vector Machines (BPSO-MTWSVM) is proposed in this paper. Firstly, introducing multiple kernel functions, the twin support vector machines based on multi-core is constructed. This strategy is a good way to solve the kernel model selection. However, it has added three adjustable parameters. In order to solve the parameters selection problem which contain TWSVM parameters and multi-core model parameters, binary PSO (BPSO) is introduced. BPSO is an optimization algorithm who has strong robustness and good global searching ability. Finally, compared with the classical TWSVM the experimental results show that BPSO-MTWSVM has higher classification accuracy.

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Ding, S.F., Qi, B.J.: Research of granular support vector machine. Artif. Intell. Rev. 38(1), 1–7 (2012). https://doi.org/10.1007/s10462-011-9235-9

    Article  Google Scholar 

  2. Borrero, L.A., Guette, L.S., Lopez, E., Pineda, O.B., Castro, E.B.: Predicting toxicity properties through machine learning. Procedia Comput. Sci. 170, 1011–1016 (2020)

    Article  Google Scholar 

  3. Jing, X.J., Cheng, L.: An optimal PID control algorithm for training feedforward neural networks. IEEE Trans. Ind. Electron. 60(6), 2273–2283 (2013)

    Article  Google Scholar 

  4. Liu, X., Jin, J., Wu, W., Herz, F.: A novel support vector machine ensemble model for estimation of free lime content in cement clinkers. ISA Trans. 99, 479–487 (2020)

    Article  Google Scholar 

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

    Google Scholar 

  6. Fung, G., Mangasarian, O.L.: Proximal support vector machine classifiers. In: Proceedings of the 7th ACM SIFKDD International Conference on Knowledge Discovery and Data Mining, pp. 77–86 (2001)

    Google Scholar 

  7. Mangasarian, O.L., Wild, E.W.: Multisurface proximal support vector machine classification via generalized eigenvalues. IEEE Trans. Pattern Anal. Mach. Intell. 28(1), 69–74 (2006)

    Article  Google Scholar 

  8. Tang, X., Ma, Z., Hu, Q., Tang, W.: A real-time arrhythmia heartbeats classification algorithm using parallel delta modulations and rotated linear-kernel support vector machines. IEEE Trans. Bio-Med. Eng. 67(4), 978–986 (2020)

    Article  Google Scholar 

  9. Sheykh Mohammadi, F., Amiri, A.: TS-WRSVM: twin structural weighted relaxed support vector machine. Connection Sci. 31(3), 215–243 (2019)

    Article  Google Scholar 

  10. Huang, H.J., Ding, S.F., Shi, Z.Z.: Primal least squares twin support vector regression. J. Zhejiang Univ.-Sci. C-Comput. Electron. 14(9), 722–732 (2013). https://doi.org/10.1631/jzus.CIIP1301

    Article  Google Scholar 

  11. Xu, Y.T., Wang, L.S.: A weighted twin support vector regression. Knowl.-Based Syst. 33, 92–101 (2012)

    Article  Google Scholar 

  12. Kumar, M.A., Khemchandani, R., Gopal, M., Chandra, S.: Knowledge based least squares twin support vector machines. Inf. Sci. 180(23), 4606–4618 (2010)

    Article  MathSciNet  Google Scholar 

  13. Ye, Q.L., Zhao, C.X., Chen, X.B.: A feature selection method for TWSVM via a regularization technique. J. Comput. Res. Dev. 48(6), 1029–1037 (2011)

    Google Scholar 

  14. Xu, Y.T., Guo, R., Wang, L.S.: A twin multi-class classification support vector machine. Cogn. Comput. 5(4), 580–588 (2012). https://doi.org/10.1007/s12559-012-9179-7

    Article  Google Scholar 

  15. Ghosh, S.K., Biswas, B., Ghosh, A.: A novel approach of retinal image enhancement using PSO system and measure of fuzziness. Procedia Comput. Sci. 167, 1300–1311 (2020)

    Article  Google Scholar 

  16. Zheng, X., Gao, Y., Jing, W., Wang, Y.: Multidisciplinary integrated design of long-range ballistic missile using PSO algorithm. J. Syst. Eng. Electron. 31(02), 335–349 (2020)

    Article  Google Scholar 

  17. Wan, P., Zou, H., Wang, K., Zhao, Z.: Research on hot deformation behavior of Zr-4 alloy based on PSO-BP artificial neural network. J. Alloy. Compd. 826 (2020)

    Google Scholar 

  18. Kennedy, J., Eberhart, R.C.: A discrete binary version of the particle swarm algorithm. In: 1997 Proceedings of the World Multiconference on Systemics, Cybernetics and Informatics, Piscataway, NJ, pp. 4104–4109 (1997)

    Google Scholar 

Download references

Acknowledgement

This work is supported by the National Natural Science Foundation of China (61662005). Guangxi Natural Science Foundation (2018GXNSFAA294068); Basic Ability Improvement Project for Young and Middle-aged Teachers in Colleges and Universities in Guangxi (2019KY0195); Research Project of Guangxi University for Nationalities (2019KJYB006).

Author information

Authors and Affiliations

  1. College of Artificial Intelligence, Guangxi University for Nationalities, Nanning, 530006, China

    Huajuan Huang & Xiuxi Wei

Authors
  1. Huajuan Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiuxi Wei
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xiuxi Wei .

Editor information

Editors and Affiliations

  1. Machine Learning and Systems Biology, Tongji University, Shanghai, China

    De-Shuang Huang

  2. School of Electrical, Computer and Telecommunications Engineering, University of Wollongong, North Wollongong, NSW, Australia

    Prashan Premaratne

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Huang, H., Wei, X. (2020). Multi-core Twin Support Vector Machines Based on Binary PSO Optimization. In: Huang, DS., Premaratne, P. (eds) Intelligent Computing Methodologies. ICIC 2020. Lecture Notes in Computer Science(), vol 12465. Springer, Cham. https://doi.org/10.1007/978-3-030-60796-8_36

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-60796-8_36

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-60795-1

  • Online ISBN: 978-3-030-60796-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation