Skip to main content
SpringerLink
Log in

A Scalable Approach to Fuzzy Rough Nearest Neighbour Classification with Ordered Weighted Averaging Operators

  • Conference paper
  • First Online:
Rough Sets (IJCRS 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11499))

Included in the following conference series:

Abstract

Fuzzy rough sets have been successfully applied in classification tasks, in particular in combination with OWA operators. There has been a lot of research into adapting algorithms for use with Big Data through parallelisation, but no concrete strategy exists to design a Big Data fuzzy rough sets based classifier. Existing Big Data approaches use fuzzy rough sets for feature and prototype selection, and have often not involved very large datasets. We fill this gap by presenting the first Big Data extension of an algorithm that uses fuzzy rough sets directly to classify test instances, a distributed implementation of FRNN-OWA in Apache Spark. Through a series of systematic tests involving generated datasets, we demonstrate that it can achieve a speedup effectively equal to the number of computing cores used, meaning that it can scale to arbitrarily large datasets.

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

References

  1. Asfoor, H., et al.: Computing fuzzy rough approximations in large scale information systems. In: 2014 IEEE International Conference on Big Data (Big Data), pp. 9–16. IEEE (2014)

    Google Scholar 

  2. Asfoor, H.M.: Fuzzy rough set approximations in large scale information systems. Master’s thesis, University of Washington (2015)

    Google Scholar 

  3. Baldi, P., Cranmer, K., Faucett, T., Sadowski, P., Whiteson, D.: Parameterized neural networks for high-energy physics. Eur. Phys. J. C 76(5) (2016). Article number 235

    Google Scholar 

  4. Baldi, P., Sadowski, P., Whiteson, D.: Searching for exotic particles in high-energy physics with deep learning. Nat. Commun. 5, 4308 (2014)

    Article  Google Scholar 

  5. Cattral, R., Oppacher, F., Deugo, D.: Evolutionary data mining with automatic rule generalization. Recent Adv. Comput. Comput. Commun. 1(1), 296–300 (2002)

    Google Scholar 

  6. Dua, D., Karra Taniskidou, E.: UCI machine learning repository (2017). http://archive.ics.uci.edu/ml

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

    Article  Google Scholar 

  8. Hu, Q., Zhang, L., Zhou, Y., Pedrycz, W.: Large-scale multimodality attribute reduction with multi-kernel fuzzy rough sets. IEEE Trans. Fuzzy Syst. 26(1), 226–238 (2018)

    Article  Google Scholar 

  9. Jensen, R., Cornelis, C.: A new approach to fuzzy-rough nearest neighbour classification. In: Chan, C.-C., Grzymala-Busse, J.W., Ziarko, W.P. (eds.) RSCTC 2008. LNCS (LNAI), vol. 5306, pp. 310–319. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-88425-5_32

    Chapter  Google Scholar 

  10. Jensen, R., Mac Parthaláin, N.: Towards scalable fuzzy-rough feature selection. Inf. Sci. 323, 1–15 (2015)

    Article  MathSciNet  Google Scholar 

  11. Karau, H., Konwinski, A., Wendell, P., Zaharia, M.: Learning Spark: Lightning-Fast Big Data Analysis. O’Reilly Media Inc, Newton (2015)

    Google Scholar 

  12. Maillo, J., Luengo, J., García, S., Herrera, F., Triguero, I.: Exact fuzzy k-nearest neighbor classification for big datasets. In: 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1–6. IEEE (2017)

    Google Scholar 

  13. Maillo, J., Ramírez, S., Triguero, I., Herrera, F.: kNN-IS: an iterative spark-based design of the k-nearest neighbors classifier for big data. Knowl.-Based Syst. 117, 3–15 (2017)

    Article  Google Scholar 

  14. Qian, Y., Wang, Q., Cheng, H., Liang, J., Dang, C.: Fuzzy-rough feature selection accelerator. Fuzzy Sets Syst. 258, 61–78 (2015)

    Article  MathSciNet  Google Scholar 

  15. Ramentol, E., et al.: IFROWANN: imbalanced fuzzy-rough ordered weighted average nearest neighbor classification. IEEE Trans. Fuzzy Syst. 23(5), 1622–1637 (2015)

    Article  Google Scholar 

  16. Verbiest, N., Cornelis, C., Herrera, F.: OWA-FRPS: a prototype selection method based on ordered weighted average fuzzy rough set theory. In: Ciucci, D., Inuiguchi, M., Yao, Y., Ślęzak, D., Wang, G. (eds.) RSFDGrC 2013. LNCS (LNAI), vol. 8170, pp. 180–190. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-41218-9_19

    Chapter  Google Scholar 

  17. Verbiest, N., Cornelis, C., Jensen, R.: Fuzzy rough positive region based nearest neighbour classification. In: 2012 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1–7. IEEE (2012)

    Google Scholar 

  18. Vluymans, S., et al.: Distributed fuzzy rough prototype selection for big data regression. In: 2015 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS) held jointly with 2015 5th World Conference on Soft Computing (WConSC), pp. 1–6. IEEE (2015)

    Google Scholar 

  19. Vluymans, S., Fernández, A., Saeys, Y., Cornelis, C., Herrera, F.: Dynamic affinity-based classification of multi-class imbalanced data with one-versus-one decomposition: a fuzzy rough set approach. Knowl. Inf. Syst. 56(1), 55–84 (2018)

    Article  Google Scholar 

  20. Vluymans, S., Sánchez Tarragó, D., Saeys, Y., Cornelis, C., Herrera, F.: Fuzzy rough classifiers for class imbalanced multi-instance data. Pattern Recognit. 53, 36–45 (2016)

    Article  Google Scholar 

  21. Yager, R.R.: On ordered weighted averaging aggregation operators in multicriteria decisionmaking. IEEE Trans. Syst. Man Cybern. 18(1), 183–190 (1988)

    Article  MathSciNet  Google Scholar 

  22. Zeng, A., Li, T., Hu, J., Chen, H., Luo, C.: Dynamical updating fuzzy rough approximations for hybrid data under the variation of attribute values. Inf. Sci. 378, 363–388 (2017)

    Article  MathSciNet  Google Scholar 

  23. 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  Google Scholar 

Download references

Acknowledgement

The research reported in this paper was conducted with the financial support of the Odysseus programme of the Research Foundation – Flanders (FWO). D. Peralta is a Postdoctoral Fellow of the Research Foundation – Flanders (FWO).

Author information

Authors and Affiliations

  1. Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium

    Oliver Urs Lenz, Daniel Peralta & Chris Cornelis

  2. Data Mining and Modelling for Biomedicine Group, VIB Center for Inflammation Research, Ghent University, Ghent, Belgium

    Daniel Peralta

Authors
  1. Oliver Urs Lenz
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Daniel Peralta
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chris Cornelis
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Oliver Urs Lenz .

Editor information

Editors and Affiliations

  1. University of Debrecen, Debrecen, Hungary

    Tamás Mihálydeák

  2. Southwest Petroleum University, Chengdu, China

    Fan Min

  3. Chongqing University of Posts and Telecommunications, Chongqing, China

    Guoyin Wang

  4. Indian Institute of Technology Kanpur, Kanpur, India

    Mohua Banerjee

  5. Fujian Normal University, Fuzhou, China

    Ivo Düntsch

  6. University of Rzeszów, Rzeszow, Poland

    Zbigniew Suraj

  7. University of Milano-Bicocca, Milan, Italy

    Davide Ciucci

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

Lenz, O.U., Peralta, D., Cornelis, C. (2019). A Scalable Approach to Fuzzy Rough Nearest Neighbour Classification with Ordered Weighted Averaging Operators. In: Mihálydeák, T., et al. Rough Sets. IJCRS 2019. Lecture Notes in Computer Science(), vol 11499. Springer, Cham. https://doi.org/10.1007/978-3-030-22815-6_16

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-22815-6_16

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-22814-9

  • Online ISBN: 978-3-030-22815-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation