Skip to main content

Research on Risk Aversion Enterprise Financial Crisis Warning Based on Support Vector Data Description

  • Conference paper
  • First Online:
Cloud Computing and Security (ICCCS 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11065))

Included in the following conference series:

  • 2297 Accesses

Abstract

Enterprise financial crisis warning is on the basis of the existing financial index to construct and run mathematical model to predict the possibility of enterprise financial crisis. Due Based on reviewing research situation of enterprise financial crisis warning both domestic and foreign, a new financial crisis warning model based on support vector data description for risk aversion enterprise is proposed which aims at the ignorance of loss differences caused by model errors from the angle of the usage of financial crisis model by the manager of risk aversion enterprises. The theoretical analysis and empirical study show that the proposed model can reduce the second class of financial crisis warning model errors.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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

Institutional subscriptions

References

  1. Altman, E.: Financial ratios: discriminant analysis and the prediction of corporate bankruptcy. J. Financ. 23, 589–609 (1986)

    Article  Google Scholar 

  2. Jie, S.: Research on intelligent decision making method of enterprise financial crisis precaution. Doctoral Dissertation of Harbin Institute of Technology (2007)

    Google Scholar 

  3. Tax, D., Duin, R.: Support vector domain description. Pattern Recognit. Lett. 20(11–13), 1191–1199 (1999)

    Article  Google Scholar 

  4. Shawe-Taylor, J., Cristianini, N.: Kernel Methods for Pattern Analysis. Cambridge University Press, Cambridge (2004)

    Book  Google Scholar 

  5. Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: a survey. ACM Comput. Surv. (CSUR) 41(3), 15 (2009)

    Article  Google Scholar 

  6. Fiolet, V., Toursel, B.: Distributed data mining. Scalable Comput.: Pract. Exp. 6(1), 99–109 (2005)

    Google Scholar 

  7. Perez, M.S., Sanchez, A., Robles, V., et al.: Design and implementation of a data mining grid-aware architecture. Futur. Gener. Comput. Syst. 23(1), 42–47 (2007)

    Article  Google Scholar 

  8. Baoan, Y., et al.: The application of BP neural network in enterprise financial crisis precaution. 2(2), 50–56 (2001)

    Google Scholar 

  9. Tax, D.M.J., Duin, R.P.W.: Support vector data description. Mach. Learn. 54(1), 45–66 (2004)

    Article  Google Scholar 

  10. Markou, M., Singh, S.: Novelty detection: a review-part 1: statistical approaches. Signal Process. 83(12), 2481–2497 (2003)

    Article  Google Scholar 

Download references

Acknowledgment

This work was supported by the National Key research and Development Plan (Grant No. 2018YFB0803504), the National Natural Science Foundation of China under Grant No. 61572153, and the key research topics of economic and social development in Heilongjiang province under Grant No. WY2017048-B.

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Xiang Yu or Hui Lu .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Yu, X., Chen, S., Li, Y., Lu, H., Wang, L. (2018). Research on Risk Aversion Enterprise Financial Crisis Warning Based on Support Vector Data Description. In: Sun, X., Pan, Z., Bertino, E. (eds) Cloud Computing and Security. ICCCS 2018. Lecture Notes in Computer Science(), vol 11065. Springer, Cham. https://doi.org/10.1007/978-3-030-00012-7_21

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-00012-7_21

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-00011-0

  • Online ISBN: 978-3-030-00012-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics