Skip to main content
SpringerLink
Log in

Introducing Fluctuation into Increasing Order of Symmetric Uncertainty for Consistency-Based Feature Selection

  • Conference paper
  • First Online:
Theory and Applications of Models of Computation (TAMC 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11436))

Abstract

In order to select correlated and relevant features in a feature selection, several filter methods adopt a symmetric uncertainty as one of the feature ranking measures. In this paper, we introduce a fluctuation into the increasing order of the symmetric uncertainty for the consistency-based feature selection algorithms. Here, the fluctuation is an operation of transforming the sorted sequence of features to a new sequence of features. Then, we compare the selected features by the algorithms with a fluctuation with those without fluctuations.

The author would like to express thanks for support by Grant-in-Aid for Scientific Research 17H00762, 16H02870 and 16H01743 from the Ministry of Education, Culture, Sports, Science and Technology, Japan.

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

Access this chapter

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 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

Notes

  1. 1.

    NIPS 2003 Workshop on Feature Extraction and Feature Selection Challenge. http://clopinet.com/isabelle/Projects/NIPS2003/#challenge.

  2. 2.

    WCCI 2004 Performance Prediction Challenge. http://clopinet.com/isabelle/Projects/modelselect/datasets/.

  3. 3.

    NCBI, National Center for Biotechnology Information. http://www.ncbi.gov/genome/FLU/.

  4. 4.

    LIBSVM: A Library for Support Vector Machine: https://www.csie.ntu.edu.tw/~cjlin/libsvm/.

References

  1. Almuallim, H., Dietterich, T.G.: Learning boolean concepts in the presence of many irrelevant features. Artif. Intell. 69, 279–305 (1994)

    Article  MathSciNet  Google Scholar 

  2. Arauzo-Azofra, A., Benitez, J.M., Castro, J.L.: Consistency measures for feature selection. J. Intell. Inf. Syst. 30, 273–292 (2008)

    Article  Google Scholar 

  3. Bolón-Canedo, V., Sánchez-Maroño, N., Alonso-Betanozos, A.: Feature Selection for High-Dimensional Data. Springer, Heidelberg (2015). https://doi.org/10.1007/978-3-319-21858-8

    Book  Google Scholar 

  4. Dash, M., Liu, H.: Consistency-based search in feature selection. Artif. Intell. 151, 155–176 (2003)

    Article  MathSciNet  Google Scholar 

  5. Hall, M.A.: Correlation-based feature selection for machine learning, Ph.D thesis, Waikato University (1998)

    Google Scholar 

  6. Quinlan, J.R.: Induction of decision trees. Mach. Learn. 1, 81–106 (1986)

    Google Scholar 

  7. Kononenko, I.: Estimating attributes: analysis and extensions of RELIEF. In: Bergadano, F., De Raedt, L. (eds.) ECML 1994. LNCS, vol. 784, pp. 171–182. Springer, Heidelberg (1994). https://doi.org/10.1007/3-540-57868-4_57

    Chapter  Google Scholar 

  8. Press, W.H., Flannery, B.P., Teukolsky, S.A., Vetterling, W.T.: Numerical recipes in C. Cambridge University Press, Cambridge (1988)

    MATH  Google Scholar 

  9. Shannon, C.E., Weaver, W.: The Mathematical Theory of Communication. University of Illinois Press, Champaign (1948)

    MATH  Google Scholar 

  10. Shin, K., Xu, X.M.: Consistency-based feature selection. In: Velásquez, J.D., Ríos, S.A., Howlett, R.J., Jain, L.C. (eds.) KES 2009. LNCS (LNAI), vol. 5711, pp. 342–350. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-04595-0_42

    Chapter  Google Scholar 

  11. Shin, K., Fernanndes, D., Miyazaki, S.: Consistency measures for feature selection: a formal definition, relative sensitivity comparison, and a fast algorithm. In: Proceedings IJCAI 2011, pp. 1491–1497. AAAI/IJCAI Press (2011)

    Google Scholar 

  12. Shin, K., Kuboyama, T., Hashimoto, T., Shepard, D.: Super-CWC and super-LCC: Super fast feature selection algorithms. In: Proceedings BigData 2015, pp. 1–7. IEEE (2015)

    Google Scholar 

  13. Shin, K., Miyazaki, S.: A fast and accurate feature selection algorithm based on binary consistency measures. Comput. Intell. 32, 646–666 (2016)

    Article  MathSciNet  Google Scholar 

  14. Webb, A.R., Kopsey, K.D.: Statistical Pattern Recognition, 3rd edn. Wiley, Hoboken (2011)

    Book  Google Scholar 

  15. Yu, L., Liu, H.: Efficient feature selection via analysis of relevance and redundancy. J. Mach. Learn. Res. (JMLR) 5, 1205–1224 (2004)

    MathSciNet  MATH  Google Scholar 

  16. Zhao, Z., Liu, H.: Searching for interacting features. In: Proceedings IJCAI 2007, pp. 1156–1161. AAAI/IJCAI Press (2007)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Graduate School of Computer Science and Systems Engineering, Kyushu Institute of Technology, Kawazu 680-4, Iizuka, 820-8502, Japan

    Sho Shimamura

  2. Department of Artificial Intelligence, Kyushu Institute of Technology, Kawazu 680-4, Iizuka, 820-8502, Japan

    Kouichi Hirata

Authors
  1. Sho Shimamura
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kouichi Hirata
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kouichi Hirata .

Editor information

Editors and Affiliations

  1. Anna University, Chennai, India

    T.V. Gopal

  2. Waseda University, Kitakyushu, Japan

    Junzo Watada

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

Shimamura, S., Hirata, K. (2019). Introducing Fluctuation into Increasing Order of Symmetric Uncertainty for Consistency-Based Feature Selection. In: Gopal, T., Watada, J. (eds) Theory and Applications of Models of Computation. TAMC 2019. Lecture Notes in Computer Science(), vol 11436. Springer, Cham. https://doi.org/10.1007/978-3-030-14812-6_34

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-14812-6_34

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-14811-9

  • Online ISBN: 978-3-030-14812-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation