Skip to main content
SpringerLink
Log in

Feature Subset Selection of Semi-supervised Data: An Intuitionistic Fuzzy-Rough Set-Based Concept

  • Conference paper
  • First Online:
Proceedings of International Ethical Hacking Conference 2018

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 811))

Abstract

We are surrounded by a spate of data generating from various sources. To extract some relevant information from these data sets, many pre-processing techniques have been proposed, in which feature selection technique is widely used. However, most of the feature selection approaches focus on supervised learning, which operates on labelled data only. In real-world applications, such as medical diagnosis, forensic science, both labelled and unlabelled data instances are available. Semi-supervised learning handles these types of situations. Some of the researchers have presented rough set as well as fuzzy-rough set-based methods for feature selection of semi-supervised data sets but these approaches have their own limitations. Intuitionistic fuzzy sets maintain a stronger potency of exhibiting information and better drawing and representing intricate ambiguities of the uncertain character of the objective world when compared with fuzzy sets, as it considers positive, the negative and hesitancy degree simultaneously. In this paper, we have proposed a novel feature selection technique for partially labelled data set based on intuitionistic fuzzy-rough set theory. Moreover, we have presented supporting theorems and proposed a novel algorithm to compute reduct based on our method. Finally, we have presented supremacy of our approach over fuzzy-rough technique by considering a partially labelled information system.

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 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
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. Webb, A.R.: Statistical pattern recognition. Wiley (2003)

    Google Scholar 

  2. Jain, A.K., Duin, R.P.W., Mao, J.: Statistical pattern recognition: a review. IEEE Trans. Pattern Anal. Mach. Intell. 22(1), 4–37 (2000)

    Article  Google Scholar 

  3. Kwak, N., Choi, C.H.: Input feature selection by mutual information based on Parzen window. IEEE Trans. Pattern Anal. Mach. Intell. 24(12), 1667–1671 (2002)

    Article  Google Scholar 

  4. Langley, P.: Selection of relevant features in machine learning. In: Proceedings of the AAAI Fall Symposium on Relevance, vol. 184, pp. 245–271, Nov, 1994

    Google Scholar 

  5. Kohavi, R., John, G.H.: Wrappers for feature subset selection. Artif. Intell. 97(1–2), 273–324 (1997)

    Article  Google Scholar 

  6. Iannarilli, F.J., Rubin, P.A.: Feature selection for multiclass discrimination via mixed-integer linear programming. IEEE Trans. Pattern Anal. Mach. Intell. 25(6), 779–783 (2003)

    Article  Google Scholar 

  7. Jäger, J., Sengupta, R., Ruzzo, W.L.: Improved gene selection for classification of microarrays. In: Pacific Symposium on Biocomputing, vol. 8, pp. 53–64, Dec 2002

    Google Scholar 

  8. Xiong, M., Fang, X., Zhao, J.: Biomarker identification by feature wrappers. Genome Res. 11(11), 1878–1887 (2001)

    Article  Google Scholar 

  9. Xing, E.P., Jordan, M.I., Karp, R.M.: Feature selection for high-dimensional genomic microarray data. In: ICML, vol. 1, pp. 601–608, June 2001

    Google Scholar 

  10. Ding, C., Peng, H.: Minimum redundancy feature selection from microarray gene expression data. J. Bioinf. Comput. Biol. 3(02), 185–205 (2005)

    Article  Google Scholar 

  11. Zhao, Z., Liu, H.: Semi-supervised feature selection via spectral analysis. In: Proceedings of the 2007 SIAM International Conference on Data Mining, pp. 641–646. Society for Industrial and Applied Mathematics, April 2007

    Chapter  Google Scholar 

  12. Xu, Z., King, I., Lyu, M.R.T., Jin, R.: Discriminative semi-supervised feature selection via manifold regularization. IEEE Trans. Neural Netw. 21(7), 1033–1047 (2010)

    Article  Google Scholar 

  13. Zhao, J., Lu, K., He, X.: Locality sensitive semi-supervised feature selection. Neurocomputing 71(10), 1842–1849 (2008)

    Article  Google Scholar 

  14. Jensen, R., Vluymans, S., Mac Parthaláin, N., Cornelis, C., Saeys, Y.: Semi-supervised fuzzy-rough feature selection. In: Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing, pp. 185–195. Springer International Publishing (2015)

    Google Scholar 

  15. Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. J. Mach. Learn. Res. 3(Mar), 1157–1182 (2003)

    Google Scholar 

  16. Dy, J.G.: Unsupervised feature selection. In: Computational Methods of Feature Selection, pp. 19–39 (2008)

    Chapter  Google Scholar 

  17. Mitra, P., Murthy, C.A., Pal, S.K.: Unsupervised feature selection using feature similarity. IEEE Trans. Pattern Anal. Mach. Intell. 24(3), 301–312 (2002)

    Article  Google Scholar 

  18. Pawlak, Z.: Rough sets. Int. J. Parallel Program. 11(5), 341–356 (1982)

    MATH  Google Scholar 

  19. Pawlak, Z., Grzymala-Busse, J., Slowinski, R., Ziarko, W.: Rough sets. Commun. ACM 38(11), 88–95 (1995)

    Article  Google Scholar 

  20. Pawlak, Z.: Rough sets: theoretical aspects of reasoning about data, vol. 9. Springer Science & Business Media (2012)

    Google Scholar 

  21. Dubois, D., Prade, H.: Putting rough sets and fuzzy sets together. In: Intelligent Decision Support, pp. 203–232. Springer Netherlands (1992)

    Google Scholar 

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

    Article  Google Scholar 

  23. Zadeh, L.A.: Fuzzy sets. information and control 8(3), 338–353 (1965)

    Article  Google Scholar 

  24. Klir, G., Yuan, B.: Fuzzy sets and fuzzy logic, vol. 4. Prentice Hall, New Jersey (1995)

    MATH  Google Scholar 

  25. Atanassov, K.T.: More on intuitionistic fuzzy sets. Fuzzy Sets Syst. 33(1), 37–45 (1989)

    Article  MathSciNet  Google Scholar 

  26. Atanassov, K.T.: Intuitionistic fuzzy sets. Fuzzy Sets Syst. 20(1), 87–96 (1986)

    Article  Google Scholar 

  27. Atanassov, K.T.: Intuitionistic Fuzzy Sets: Theory and Applications. Physica-Verlag (1999)

    Google Scholar 

  28. Chakrabarty, K., Gedeon, T., Koczy, L.: Intuitionistic fuzzy rough set. In: Proceedings of 4th Joint Conference on Information Sciences (JCIS), Durham, NC, pp. 211–214 (1998)

    Google Scholar 

  29. Cornelis, C., De Cock, M., Kerre, E.E.: Intuitionistic fuzzy rough sets: at the crossroads of imperfect knowledge. Expert Syst. 20(5), 260–270 (2003)

    Article  Google Scholar 

  30. Zhang, X., Zhou, B., Li, P.: A general frame for intuitionistic fuzzy rough sets. Inf. Sci. 216, 34–49 (2012)

    Article  MathSciNet  Google Scholar 

  31. Huang, B., Zhuang, Y.L., Li, H.X., Wei, D.K.: A dominance intuitionistic fuzzy-rough set approach and its applications. Appl. Math. Model. 37(12), 7128–7141 (2013)

    Article  MathSciNet  Google Scholar 

  32. Zhang, Z.: Attributes reduction based on intuitionistic fuzzy rough sets. J. Intell. Fuzzy Syst. 30(2), 1127–1137 (2016)

    Article  Google Scholar 

  33. Iancu, I.: Intuitionistic fuzzy similarity measures based on Frank t-norms family. Pattern Recogn. Lett. 42, 128–136 (2014)

    Article  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  35. Jensen, R., Shen, Q.: Computational Intelligence and Feature Selection: Rough and Fuzzy Approaches, vol. 8. Wiley (2008)

    Google Scholar 

  36. Jurio, A., Paternain, D., Bustince, H., Guerra, C., Beliakov, G.: A construction method of Atanassov’s intuitionistic fuzzy sets for image processing. In: 2010 5th IEEE International Conference on Intelligent Systems (IS), pp. 337–342. IEEE, July 2010

    Google Scholar 

  37. De, S.K., Biswas, R., Ranjan Roy, A.: Intuitionistic fuzzy database. In: Second International Conference on IFS, NIFS, vol. 4. no. 2 (1998)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Varanasi, India

    Shivam Shreevastava, Anoop Tiwari & Tanmoy Som

Authors
  1. Shivam Shreevastava
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Anoop Tiwari
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tanmoy Som
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Anoop Tiwari .

Editor information

Editors and Affiliations

  1. Department of Information Technology, Institute of Engineering and Management, Kolkata, India

    Mohuya Chakraborty

  2. Institute of Engineering and Management, Kolkata, India

    Satyajit Chakrabarti

  3. Department of Automation and Applied Informatics, Aurel Vlaicu University of Arad, Arad, Romania

    Valentina Emilia Balas

  4. Department of Computer Science and Engineering, University of Kalyani, Kalyani, West Bengal, India

    J. K. Mandal

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Shreevastava, S., Tiwari, A., Som, T. (2019). Feature Subset Selection of Semi-supervised Data: An Intuitionistic Fuzzy-Rough Set-Based Concept. In: Chakraborty, M., Chakrabarti, S., Balas, V., Mandal, J. (eds) Proceedings of International Ethical Hacking Conference 2018. Advances in Intelligent Systems and Computing, vol 811. Springer, Singapore. https://doi.org/10.1007/978-981-13-1544-2_25

Download citation

  • DOI: https://doi.org/10.1007/978-981-13-1544-2_25

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-1543-5

  • Online ISBN: 978-981-13-1544-2

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation