Skip to main content

Multiple Reducts Computation in Rough Sets with Applications to Ensemble Classification

  • Conference paper
  • First Online:
Proceedings of ICETIT 2019

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 605))

Abstract

Rough set theory has emerged as an influential soft-computing approach for feature subset selection (reduct computation) in the decision system amidst incompleteness and inconsistency. Multiple reducts computation using rough sets provide an elegant way for construction of ensemble classifier for better and stable classification. The existing approaches for multiple reducts computation are primarily based on the genetic algorithm and select diverse multiple reducts after generation of abundant candidate reducts. This work proposes an MRGA_MRC algorithm for multiple reducts computation by utilizing the systematically evolving search space of all reducts computation in the MRGA algorithm without generation of many candidate reducts. A novel heuristic is introduced for selection of diverse multiple reducts. Experiments conducted on the benchmark decision systems have established the relevance of the proposed approach in comparison to the genetic algorithm based multiple reducts computation approach REUCS.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 259.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 329.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 329.99
Price excludes VAT (USA)
  • Durable hardcover 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. Bazan, J.G., Nguyen, H.S., Nguyen, S.H., Synak, P., Wróblewski, J.: Rough set algorithms in classification problem. In: Rough Set Methods and Applications, pp. 49–88. Springer (2000)

    Google Scholar 

  2. Beaubouef, T., Petry, F.E., Arora, G.: Information-theoretic measures of uncertainty for rough sets and rough relational databases. Inf. Sci. 109(1–4), 185–195 (1998)

    Article  Google Scholar 

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

    Article  Google Scholar 

  4. Brown, G., Wyatt, J.L., Harris, R., Yao, X.: Diversity creation methods: a survey and categorisation. Inf. Fusion 6(1), 5–20 (2005)

    Article  Google Scholar 

  5. Cohen, W.W.: Fast effective rule induction. In: Proceedings of the Twelfth International Conference on Machine Learning, pp. 115–123. Morgan Kaufmann (1995)

    Google Scholar 

  6. Das, A.K., Sil, J.: An efficient classifier design integrating rough set and set oriented database operations. Appl. Soft Comput. 11(2), 2279–2285 (2011)

    Article  Google Scholar 

  7. Debie, E.S., Shafi, K., Lokan, C., Merrick, K.E.: Reduct based ensemble of learning classifier system for real-valued classification problems. In: CIEL, pp. 66–73. IEEE (2013)

    Google Scholar 

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

  9. Elkan, C.: Boosting and Naıve Bayesian learning. In: Proceedings of KDD 1997, New Port Beach, CA (1997)

    Google Scholar 

  10. Gao, S., Dai, J., Shi, H.: Discernibility matrix-based ensemble learning. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 952–957. IEEE Computer Society (2018)

    Google Scholar 

  11. Guan, J., Bell, D.A.: Rough computational methods for information systems. Artif. Intell. 105(1–2), 77–103 (1998)

    Article  Google Scholar 

  12. Hu, X., Cercone, N., Ziarko, W.: Generation of multiple knowledge from databases based on rough sets theory. In: Rough Sets and Data Mining, pp. 109–121. Springer (1997)

    Google Scholar 

  13. Ponti Jr., M.P.: Combining classifiers: from the creation of ensembles to the decision fusion. In: 2011 24th SIBGRAPI Conference on Graphics, Patterns, and Images Tutorials, pp. 1–10. IEEE, August 2011

    Google Scholar 

  14. Kaneiwa, K., Kudo, Y.: A sequential pattern mining algorithm using rough set theory. Int. J. Approx. Reason. 52(6), 881–893 (2011)

    Article  Google Scholar 

  15. Kuncheva, L.I., Skurichina, M., Duin, R.P.: An experimental study on diversity for bagging and boosting with linear classifiers. Inf. Fusion 3(4), 245–258 (2002)

    Article  Google Scholar 

  16. Liang, J., Shi, Z.: The information entropy, rough entropy and knowledge granulation in rough set theory. Int. J. Uncertainty Fuzziness Knowl.-Based Syst. 12(01), 37–46 (2004)

    Article  MathSciNet  Google Scholar 

  17. Lin, T., Cercone, N.: Rough sets and data mining: analysis of imprecise data (1996)

    Book  Google Scholar 

  18. Nguyen, H.S.: Approximate boolean reasoning: foundations and applications in data mining. In: Peters, J.F., Skowron, A. (eds.) Transactions on Rough Sets V, pp. 334–506. Springer, Heidelberg (2006)

    Google Scholar 

  19. Nguyen, H.S.: Rough sets and boolean reasoning. In: Rough Computing: Theories, Technologies and Applications, pp. 38–69. IGI Global (2008)

    Google Scholar 

  20. Øhrn, A., Komorowski, J., et al.: Rosetta–a rough set toolkit for analysis of data. In: Proceedings of the Third International Joint Conference on Information Sciences. Citeseer (1997)

    Google Scholar 

  21. Pan, X., Zhang, S.: Ensemble remote sensing classifier based on rough set theory and genetic algorithm. In: 2010 18th International Conference on Geoinformatics, pp. 1–5, June 2010

    Google Scholar 

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

    Article  Google Scholar 

  23. Pawlak, Z., Sets, R.: Theoretical Aspects of Reasoning About Data. Kluwer, Netherlands (1991)

    Google Scholar 

  24. Sai Prasad, P.S.V.S., Rao, C.R.: IQuickrRduct: an improvement to quick reduct algorithm. In: Sakai, H., Chakraborty, M.K., Hassanien, A.E., Slezak, D., Zhu, W. (eds.) RSFDGrC. LNCS, vol. 5908, pp. 152–159. Springer (2009)

    Google Scholar 

  25. Sai Prasad, P.S.V.S., Rao, C.R.: Extensions to IQuickReduct. In: Sombattheera, C., Agarwal, A., Udgata, S.K., Lavangnananda, K. (eds.) MIWAI. LNCS, vol. 7080, pp. 351–362. Springer (2011)

    Google Scholar 

  26. Shi, L., Duan, Q., Zhang, J., Xi, L., Ma, X.: Rough set based ensemble learning algorithm for agricultural data classification. Filomat 32(5), 1917–1930 (2018)

    Article  Google Scholar 

  27. Skowron, A., Rauszer, C.: The Discernibility Matrices and Functions in Information Systems, pp. 331–362. Springer, Dordrecht (1992)

    Chapter  Google Scholar 

  28. GraphPad Software: GraphPad t-test calculator (2018). https://www.graphpad.com/quickcalcs/ttest1/l

  29. Susmaga, R.: Parallel computation of reducts. In: Polkowski, L., Skowron, A. (eds.) Rough Sets and Current Trends in Computing, pp. 450–458. Springer, Heidelberg (1998)

    Chapter  Google Scholar 

  30. Trabelsi, A., Elouedi, Z., Lefevre, E.: Ensemble enhanced evidential K-NN classifier through rough set reducts. In: Medina, J., Ojeda-Aciego, M., Galdeano, J.L.V., Pelta, D.A., Cabrera, I.P., Bouchon-Meunier, B., Yager, R.R. (eds.) IPMU (1). CCIS, vol. 853, pp. 383–394. Springer (2018)

    Google Scholar 

  31. Wang, J., Wang, J.: Reduction algorithms based on discernibility matrix: the ordered attributes method. J. Comput. Sci. Technol. 16(6), 489–504 (2001)

    Article  MathSciNet  Google Scholar 

  32. Wong, S., Ziarko, W.: On optimal decision rules in decision tables. University of Regina, Computer Science Department (1985)

    Google Scholar 

  33. Wroblewski, J.: Finding minimal reducts using genetic algorithms. In: Proceedings of the Second Annual Join Conference on Information Science, vol. 2, pp. 186–189 (1995)

    Google Scholar 

  34. Wu, Q., Bell, D.A., McGinnity, T.M.: Multiknowledge for decision making. Knowl. Inf. Syst. 7(2), 246–266 (2005)

    Article  Google Scholar 

  35. Yang, X., Yao, Y.: Ensemble selector for attribute reduction. Appl. Soft Comput. 70, 1–11 (2018)

    Article  Google Scholar 

  36. Zhou, Z.H., Wu, J., Tang, W.: Ensembling neural networks: many could be better than all. Artif. Intell. 137(1–2), 239–263 (2002)

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Abhimanyu Bar .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Bar, A., Sai Prasad, P.S.V.S. (2020). Multiple Reducts Computation in Rough Sets with Applications to Ensemble Classification. In: Singh, P., Panigrahi, B., Suryadevara, N., Sharma, S., Singh, A. (eds) Proceedings of ICETIT 2019. Lecture Notes in Electrical Engineering, vol 605. Springer, Cham. https://doi.org/10.1007/978-3-030-30577-2_39

Download citation

Publish with us

Policies and ethics