Skip to main content
SpringerLink
Log in

An Efficient Gaussian Kernel Based Fuzzy-Rough Set Approach for Feature Selection

  • Conference paper
  • First Online:
Multi-disciplinary Trends in Artificial Intelligence (MIWAI 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10053))

Abstract

Fuzzy-rough set based feature selection is highly useful for reducing data dimensionality of a hybrid decision system, but the reduct computation is computationally expensive. Gaussian kernel based fuzzy rough sets merges kernel method to fuzzy-rough sets for efficient feature selection. This works aims at improving the computational performance of existing reduct computation approach in Gaussian kernel based fuzzy rough sets by incorporation of vectorized (matrix, sub-matrix) operations. The proposed approach was extensively compared by experimentation with the existing approach and also with a fuzzy rough set based reduct approaches available in Rough set R package. Results establish the relevance of proposed modifications.

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

Similar content being viewed by others

References

  1. Bhatt, R.B., Gopal, M.: On the compact computational domain of fuzzy-rough sets. Pattern Recognit. Lett. 26(11), 1632–1640 (2005)

    Article  Google Scholar 

  2. Chouchoulas, A., Shen, Q.: Rough set-aided keyword reduction for text categorization. Appl. Artif. Intell. 15(9), 843–873 (2001)

    Article  Google Scholar 

  3. Cornelis, C., Jensen, R., Hurtado, G., Śle, D., et al.: Attribute selection with fuzzy decision reducts. Inf. Sci. 180(2), 209–224 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  4. Dubois, D., Prade, H.: Rough fuzzy sets and fuzzy rough sets*. Int. J. Gen. Syst. 17(2–3), 191–209 (1990)

    Article  MATH  Google Scholar 

  5. Hu, Q., Zhang, L., Chen, D., Pedrycz, W., Yu, D.: Gaussian kernel based fuzzy rough sets: model, uncertainty measures and applications. Int. J. Approx. Reason. 51(4), 453–471 (2010)

    Article  MATH  Google Scholar 

  6. Jensen, R., Shen, Q.: Fuzzy-rough attribute reduction with application to web categorization. Fuzzy Sets Syst. 141(3), 469–485 (2004)

    Article  MathSciNet  MATH  Google Scholar 

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

    Article  Google Scholar 

  8. Lichman, M.: UCI machine learning repository (2013)

    Google Scholar 

  9. Moser, B.: On representing and generating kernels by fuzzy equivalence relations. J. Mach. Learn. Res. 7, 2603–2620 (2006)

    MathSciNet  MATH  Google Scholar 

  10. Pawlak, Z.: Rough sets. Int. J. Comput. Inf. Sci. 11(5), 341–356 (1982)

    Article  MathSciNet  MATH  Google Scholar 

  11. Pawlak, Z., Skowron, A.: Rough sets: some extensions. Inf. Sci. 177(1), 28–40 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  12. Radzikowska, A.M., Kerre, E.E.: A comparative study of fuzzy rough sets. Fuzzy Sets Syst. 126(2), 137–155 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  13. Riza, L.S., Janusz, A., Slezak, D., Cornelis, C., Herrera, F., Benitez, J.M., Bergmeir, C., Stawicki, S.: Package roughsets

    Google Scholar 

  14. Prasad, P.S.V.S.S., Rao, C.R.: IQuickReduct: an improvement to quick reduct algorithm. In: Sakai, H., Chakraborty, M.K., Hassanien, A.E., Ślȩzak, D., Zhu, W. (eds.) RSFDGrC 2009. LNCS (LNAI), vol. 5908, pp. 152–159. Springer, Heidelberg (2009). doi:10.1007/978-3-642-10646-0_18

    Chapter  Google Scholar 

  15. Sai Prasad, P.S.V.S., Raghavendra Rao, C.: An efficient approach for fuzzy decision reduct computation. In: Peters, J.F., Skowron, A. (eds.) Transactions on Rough Sets XVII. LNCS, vol. 8375, pp. 82–108. Springer, Heidelberg (2014). doi:10.1007/978-3-642-54756-0_5

    Chapter  Google Scholar 

  16. Wang, H., Padua, D., Wu, P.: Vectorization of apply to reduce interpretation overhead of R. In: ACM SIGPLAN Notices, vol. 50, pp. 400–415. ACM (2015)

    Google Scholar 

  17. Zadeh, L.A.: Fuzzy sets. Inf. Control 8(3), 338–353 (1965)

    Article  MathSciNet  MATH  Google Scholar 

  18. Zeng, A., Li, T., Liu, D., Zhang, J., Chen, H.: A fuzzy rough set approach for incremental feature selection on hybrid information systems. Fuzzy Sets Syst. 258, 39–60 (2015)

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computer and Information Sciences, University of Hyderabad, Hyderabad, India

    Soumen Ghosh, P. S. V. S. Sai Prasad & C. Raghavendra Rao

Authors
  1. Soumen Ghosh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. P. S. V. S. Sai Prasad
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. C. Raghavendra Rao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to P. S. V. S. Sai Prasad .

Editor information

Editors and Affiliations

  1. Mahasarakham University , Maha Sarakham, Thailand

    Chattrakul Sombattheera

  2. Harz University of Applied Sciences , Wernigerode, Germany

    Frieder Stolzenburg

  3. University of Science and Technology , Hong Kong, China

    Fangzhen Lin

  4. Macquarie University , North Ryde, New South Wales, Australia

    Abhaya Nayak

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing AG

About this paper

Cite this paper

Ghosh, S., Sai Prasad, P.S.V.S., Rao, C.R. (2016). An Efficient Gaussian Kernel Based Fuzzy-Rough Set Approach for Feature Selection. In: Sombattheera, C., Stolzenburg, F., Lin, F., Nayak, A. (eds) Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2016. Lecture Notes in Computer Science(), vol 10053. Springer, Cham. https://doi.org/10.1007/978-3-319-49397-8_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-49397-8_4

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-49396-1

  • Online ISBN: 978-3-319-49397-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation