Skip to main content
SpringerLink
Log in
Book cover

International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems

IEA/AIE 2019: Advances and Trends in Artificial Intelligence. From Theory to Practice pp 50–62Cite as

Predicting the Listing Status of Chinese Listed Companies Using Twin Multi-class Classification Support Vector Machine

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11606))

Abstract

Multi-class classification problem is research challenge in many applications. Listing companies’ statuses are signals on different risk levels in China’s stock markets. The prediction of the listing statuses is complex problem due to imbalance in the data, due to different values and features. In the literature when the list status is divided into two categories for simple measurements using binary classification model, accurate risk management cannot achieved correctly. In this work, we have used SMOTE and wrapper feature selection to reprocess data. Accordingly, we have proposed an algorithm named as Twin-KSVC (twin multi-class support vector machine) which is used for multi-class classification problem by “1-versus-1-versus-rest” structure. Our experiments tested on large sample of data set; show that we could achieve better performance, in comparison with other approach. We have tested our algorithm on different strategies of feature selection for comparison purposes.

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   89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   119.99
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. Cheng, E., Xia, F., Wang, G.Y.: The special treatment designation and information transmission in the Chinese stock market. Math. Methods Financ. Bus. Adm. 139–151 (2014)

    Google Scholar 

  2. Zhou, L., Tama, K.P., Fujita, H.: Predicting the listing status of Chinese listed companies with multi-class classification models. Inf. Sci. 328, 222–236 (2016)

    Article  Google Scholar 

  3. Zhou, L., Si, Y.-W., Fujita, H.: Predicting the listing statuses of Chinese-listed companies using decision trees combined with an improved filter feature selection method. Knowl.-Based Syst. 128, 93–101 (2017)

    Article  Google Scholar 

  4. Zhou, L., Wang, Q., Fujita, H.: One versus one multi-class classification fusion using optimizing decision directed acyclic graph for predicting listing status of companies. Inf. Fusion 36, 80–89 (2017)

    Article  Google Scholar 

  5. Hsu, C.-W., Lin, C.-J.: A comparison of methods for multiclass support vector machines. IEEE Trans. Neural Netw. 13(2), 415–425 (2002)

    Article  Google Scholar 

  6. Jayadeva, R., Khemchandani, S.C.: Twin support vector machines for pattern classification. IEEE Comput. Soc. 29(5), 905–910 (2007)

    MATH  Google Scholar 

  7. Panga, X., Xub, C., Xu, Y.: Scaling KNN multi-class twin support vector machine via safe instance reduction. Knowl.-Based Syst. 148, 17–30 (2018)

    Article  Google Scholar 

  8. Angulo, C., Parra, X., Català, A.: K-SVCR. A support vector machine for multi-class classification. Neurocomputing 55(1–2), 57–77 (2003)

    Article  Google Scholar 

  9. Xu, Y., Guo, R., Wang, L.: A twin multi-class classification support vector machine. Cogn. Comput. 5(4), 580–588 (2013)

    Article  Google Scholar 

  10. Ding, S., Yu, J., Zhao, H.: Twin support vector machines based on particle swarm optimization. J. Comput. 8(9), 2296–2303 (2013)

    Article  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  12. Muller, K.R., Mika, S., Ratsch, G., Tsuda, K., Scholkopf, B.: An Introduction to kernel-based learning algorithms. IEEE Trans. Neural Netw. 12(2), 199–222 (2001)

    Article  Google Scholar 

  13. Tomar, D., Agarwal, S.: Twin support vector machine: a review from 2007 to 2014. Egypt. Inform. J. 16(1), 55–69 (2015)

    Article  Google Scholar 

  14. Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321–357 (2002)

    Article  Google Scholar 

  15. Gao, T.: Hybrid classification approach of SMOTE and instance selection for imbalanced datasets. Iowa State University (2015). https://lib.dr.iastate.edu/etd/14331

  16. Panthonga, R., Srivihokb, A.: Wrapper feature subset selection for dimension reduction based on ensemble learning algorithm. Procedia Comput. Sci. 72, 162–169 (2015)

    Article  Google Scholar 

  17. Chandrashekar, G., Sahin, C.G.: A survey on feature selection methods. Comput. Electr. Eng. 40, 16–28 (2014)

    Article  Google Scholar 

  18. Last, M., Kandel, A., Maimon, O.: Information-theoretic algorithm for feature selection. Pattern Recogn. Lett. 22, 799–811 (2001)

    Article  Google Scholar 

  19. Kohavi, R., John, G.H.: Wrappers for feature subset selection. Artif. Intell. 97, 273–324 (1997)

    Article  Google Scholar 

  20. Zhaoa, J., Xu, Y., Fujita, H.: An improved non-parallel Universum support vector machine and its safe sample screening rule. Knowl.-Based Syst. 170, 79–88 (2019). https://doi.org/10.1016/j.knosys.2019.01.031

    Article  Google Scholar 

  21. Zhang, C., et al.: Multi-imbalance: an open-source software for multi-class imbalance learning. Knowl.-Based Syst. https://doi.org/10.1016/j.knosys.2019.03.001

Download references

Author information

Authors and Affiliations

  1. Faculty of Software and Information Science, Iwate Prefectural University, Iwate, 0200693, Japan

    Sining Zhao & Hamido Fujita

Authors
  1. Sining Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hamido Fujita
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hamido Fujita .

Editor information

Editors and Affiliations

  1. Institute for Software Technology, Graz University of Technology, Graz, Austria

    Franz Wotawa

  2. Department of Applied Informatics, University of Klagenfurt, Klagenfurt, Austria

    Gerhard Friedrich

  3. Institute for Software Technology, Graz University of Technology, Graz, Austria

    Ingo Pill

  4. Institute for Software Technology, Graz University of Technology, Graz, Austria

    Roxane Koitz-Hristov

  5. Department of Computer Science, Texas State University, San Marcos, TX, USA

    Moonis Ali

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zhao, S., Fujita, H. (2019). Predicting the Listing Status of Chinese Listed Companies Using Twin Multi-class Classification Support Vector Machine. In: Wotawa, F., Friedrich, G., Pill, I., Koitz-Hristov, R., Ali, M. (eds) Advances and Trends in Artificial Intelligence. From Theory to Practice. IEA/AIE 2019. Lecture Notes in Computer Science(), vol 11606. Springer, Cham. https://doi.org/10.1007/978-3-030-22999-3_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-22999-3_5

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-22998-6

  • Online ISBN: 978-3-030-22999-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation