Skip to main content
Springer Nature Link
Log in

Feature Selection Based on Fuzzy Conditional Distinction Degree

  • Conference paper
  • First Online:
Neural Information Processing (ICONIP 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11304))

Included in the following conference series:

  • 2263 Accesses

Abstract

Previous studies have shown that information entropy and its variants are useful at reducing data dimensionality. Yet, most existing approaches based on entropy exploit the correlations between features and labels, lacking of taking into account the relevance between features. In this paper, we propose a new index for feature selection, named fuzzy conditional distinction degree (FDD), based on fuzzy similarity relation by combining feature correlations with the relationship between features and labels. Different from existing approaches based on entropy, FDD considers the cardinality of the relation matrix instead of the similarity classes. Meanwhile, we encode the feature correlations into distance to measure the relevance of any two features. Some useful properties are discussed. Based on the FDD, a greedy forward algorithm for feature selection is presented. Experimental results on benchmark data sets denote the feasibility and effectiveness of the proposed approach.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    https://archive.ics.uci.edu/ml/datasets.html.

  2. 2.

    http://www.ntu.edu.sg/home/elhchen/data.htm.

References

  1. Battiti, R.: Using mutual information for selecting features in supervised neural net learning. IEEE Trans. Neural Netw. 5(4), 537–550 (1994)

    Article  Google Scholar 

  2. Dai, J., Wang, W., Xu, Q.: An uncertainty measure for incomplete decision tables and its applications. IEEE Trans. Cybern. 43(4), 1277–1289 (2013)

    Article  Google Scholar 

  3. Dai, J., Xu, Q.: Attribute selection based on information gain ratio in fuzzy rough set theory with application to tumor classification. Appl. Soft Comput. J. 13(1), 211–221 (2013)

    Article  Google Scholar 

  4. Dai, J., Xu, Q., Wang, W., Tian, H.: Conditional entropy for incomplete decision systems and its application in data mining. Int. J. Gen. Syst. 41(7), 713–728 (2012)

    Article  MathSciNet  Google Scholar 

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

    MathSciNet  MATH  Google Scholar 

  6. Hall, M.A.: Correlation-based feature selection for discrete and numeric class machine learning. In: Proceedings of the Seventeenth International Conference on Machine Learning (ICML 2000), pp. 359–366 (2000)

    Google Scholar 

  7. Hu, Q., Yu, D., Xie, Z., Liu, J.: Fuzzy probabilistic approximation spaces and their information measures. IEEE Trans. Fuzzy Syst. 14(2), 191–201 (2006)

    Article  Google Scholar 

  8. Hu, Q., Zhang, L., Zhang, D., Pan, W., An, S., Pedrycz, W.: Measuring relevance between discrete and continuous features based on neighborhood mutual information. Expert Syst. Appl. 38(9), 10737–10750 (2011)

    Article  Google Scholar 

  9. Jensen, R., Shen, Q.: New approaches to fuzzy-rough feature selection. IEEE Trans. Fuzzy Syst. 17(4), 824–838 (2009)

    Article  Google Scholar 

  10. Tallón-Ballesteros, A.J., Riquelme, J.C.: Tackling ant colony optimization meta-heuristic as search method in feature subset selection based on correlation or consistency measures. In: Corchado, E., Lozano, J.A., Quintián, H., Yin, H. (eds.) IDEAL 2014. LNCS, vol. 8669, pp. 386–393. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10840-7_47

    Chapter  Google Scholar 

  11. Tang, J., Liu, H.: Unsupervised feature selection for linked social media data. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 904–912 (2012)

    Google Scholar 

  12. Tiwari, A.K., Shreevastava, S., Som, T., Shukla, K.K.: Tolerance-based intuitionistic fuzzy-rough set approach for attribute reduction. Expert Syst. Appl. 101, 205–212 (2018)

    Article  Google Scholar 

  13. Wang, C., Hu, Q., Wang, X., Chen, D., Qian, Y., Dong, Z.: Feature selection based on neighborhood discrimination index. IEEE Trans. Neural Netw. Learn. Syst. 29(7), 2986–2999 (2017)

    MathSciNet  Google Scholar 

  14. Wang, C., Qi, Y., Shao, M., Hu, Q., Chen, D., Qian, Y., Lin, Y.: A fitting model for feature selection with fuzzy rough sets. IEEE Trans. Fuzzy Syst. 25(4), 741–753 (2017)

    Article  Google Scholar 

  15. Wang, C., Shao, M., He, Q., Qian, Y., Qi, Y.: Feature subset selection based on fuzzy neighborhood rough sets. Knowl.-Based Syst. 111, 173–179 (2016)

    Article  Google Scholar 

  16. Witten, I.H., Eibe, F., Hall, M.A.: Data Mining: Practical Machine Learning Tools and Techniques, 3rd edn. Morgan Kaufmann, Elsevier, Burlington (2011)

    MATH  Google Scholar 

  17. Yager, R.R.: Entropy measures under similarity relations. Int. J. Gen. Syst. 20(4), 341–358 (1992)

    Article  Google Scholar 

  18. Yu, L., Liu, H.: Feature selection for high-dimensional data: A fast correlation-based filter solution. In: Machine Learning, Proceedings of the Twentieth International Conference (ICML 2003), August 21–24, 2003, Washington, DC, pp. 856–863 (2003)

    Google Scholar 

Download references

Acknowledgments

This work was partially supported by the National Natural Science Foundation of China (Nos. 61473259, 61502335, 61070074, 60703038) and the Hunan Provincial Science and Technology Project Foundation (2018TP1018, 2018RS3065).

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Tianjin University, Tianjin, 300350, China

    Qilai Zhang

  2. Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, College of Information Science and Engineering, Hunan Normal University, Changsha, 410081, Hunan, China

    Jianhua Dai

Authors
  1. Qilai Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jianhua Dai
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jianhua Dai .

Editor information

Editors and Affiliations

  1. The Chinese Academy of Sciences, Beijing, China

    Long Cheng

  2. City University of Hong Kong, Kowloon, Hong Kong

    Andrew Chi Sing Leung

  3. Kobe University, Kobe, Japan

    Seiichi Ozawa

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

Zhang, Q., Dai, J. (2018). Feature Selection Based on Fuzzy Conditional Distinction Degree. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11304. Springer, Cham. https://doi.org/10.1007/978-3-030-04212-7_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-04212-7_7

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-04211-0

  • Online ISBN: 978-3-030-04212-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics