Skip to main content

Fast Algorithm of Attribute Reduction Based on the Complementation of Boolean Function

  • Chapter

Part of the book series: Topics in Intelligent Engineering and Informatics ((TIEI,volume 6))

Abstract

In this chapter we propose a new method of solving the attribute reduction problem. Our method is different to the classical approach using the so-called discernibility function and its CNF into DNF transformation. We have proved that the problem is equivalent to very efficient unate complementation algorithm. That is why we propose new algorithm based on recursive execution of the procedure, which at every step of recursion selects the splitting variable and then calculates the cofactors with respect to the selected variables (Shannon expansion procedure). The recursion continues until at each leaf of the recursion tree the easily computable rules for complement process can be applied. The recursion process creates a binary tree so that the final result is obtained merging the results in the subtrees. The final matrix represents all the minimal reducts of a decision table or all the minimal dependence sets of input variables, respectively. According to the results of computer tests, better results can be achieved by application of our method in combination with the classical method.

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

Buying options

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 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
Hardcover Book
USD   169.99
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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Abdullah, S., Golafshan, L., Nazri, M.Z.A.: Re-heat simulated annealing algorithm for rough set attribute reduction. International Journal of the Physical Sciences 6(8), 2083–2089 (2011), doi:10.5897/IJPS11.218

    Google Scholar 

  2. Bazan, J., Nguyen, H.S., Nguyen, S.H., Synak, P., Wróblewski, J.: Rough set algorithms in classification problem. In: Rough Set Methods and Applications: New Developments in Knowledge Discovery in Information Systems, vol. 56, pp. 49–88. Physica-Verlag, Heidelberg (2000), doi:10.1007/978-3-7908-1840-6_3

    Chapter  Google Scholar 

  3. Borowik, G., Łuba, T.: Attribute reduction based on the complementation of boolean functions. In: 1st Australian Conference on the Applications of Systems Engineering, ACASE 2012, Sydney, Australia, pp. 58–59 (2012) (electronic document)

    Google Scholar 

  4. Borowik, G., Łuba, T., Zydek, D.: Features reduction using logic minimization techniques. International Journal of Electronics and Telecommunications 58(1), 71–76 (2012), doi:10.2478/v10177-012-0010-x

    Article  Google Scholar 

  5. Brayton, R.K., Hachtel, G.D., McMullen, C.T., Sangiovanni-Vincentelli, A.: Logic Minimization Algorithms for VLSI Synthesis. Kluwer Academic Publishers (1984)

    Google Scholar 

  6. Brzozowski, J.A., Łuba, T.: Decomposition of boolean functions specified by cubes. Journal of Multi-Valued Logic & Soft Computing 9, 377–417 (2003)

    MATH  Google Scholar 

  7. Dash, R., Dash, R., Mishra, D.: A hybridized rough-PCA approach of attribute reduction for high dimensional data set. European Journal of Scientific Research 44(1), 29–38 (2010)

    Google Scholar 

  8. Feixiang, Z., Yingjun, Z., Li, Z.: An efficient attribute reduction in decision information systems. In: International Conference on Computer Science and Software Engineering, Wuhan, Hubei, pp. 466–469 (2008), doi:10.1109/CSSE.2008.1090

    Google Scholar 

  9. Grzenda, M.: Prediction-oriented dimensionality reduction of industrial data sets. In: Mehrotra, K.G., Mohan, C.K., Oh, J.C., Varshney, P.K., Ali, M. (eds.) IEA/AIE 2011, Part I. LNCS, vol. 6703, pp. 232–241. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  10. Hedar, A.R., Wang, J., Fukushima, M.: Tabu search for attribute reduction in rough set theory. Journal of Soft Computing – A Fusion of Foundations, Methodologies and Applications 12(9), 909–918 (2008), doi:10.1007/s00500-007-0260-1

    MATH  Google Scholar 

  11. Jensen, R., Shen, Q.: Semantics-preserving dimensionality reduction: Rough and fuzzy-rough based approaches. IEEE Transactions on Knowledge and Data Engineering 16, 1457–1471 (2004), doi:10.1109/TKDE.2004.96

    Article  Google Scholar 

  12. Jing, S., She, K.: Heterogeneous attribute reduction in noisy system based on a generalized neighborhood rough sets model. World Academy of Science, Engineering and Technology 75, 1067–1072 (2011)

    Google Scholar 

  13. Kalyani, P., Karnan, M.: A new implementation of attribute reduction using quick relative reduct algorithm. International Journal of Internet Computing 1(1), 99–102 (2011)

    Google Scholar 

  14. Kryszkiewicz, M., Cichoń, K.: Towards scalable algorithms for discovering rough set reducts. In: Peters, J.F., Skowron, A., Grzymała-Busse, J.W., Kostek, B.Z., Swiniarski, R.W., Szczuka, M.S. (eds.) Transactions on Rough Sets I. LNCS, vol. 3100, pp. 120–143. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  15. Kryszkiewicz, M., Lasek, P.: FUN: Fast discovery of minimal sets of attributes functionally determining a decision attribute. In: Peters, J.F., Skowron, A., Rybiński, H. (eds.) Transactions on Rough Sets IX. LNCS, vol. 5390, pp. 76–95. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  16. Lewandowski, J., Rawski, M., Rybiński, H.: Application of parallel decomposition for creation of reduced feed-forward neural networks. In: Kryszkiewicz, M., Peters, J.F., Rybiński, H., Skowron, A. (eds.) RSEISP 2007. LNCS (LNAI), vol. 4585, pp. 564–573. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  17. Lin, T.Y., Yao, Y.Y., Zadeh, L.A. (eds.): Data mining, rough sets and granular computing. Physica-Verlag GmbH, Heidelberg (2002)

    MATH  Google Scholar 

  18. Łuba, T., Lasocki, R.: On unknown attribute values in functional dependencies. In: Proceedings of The Third International Workshop on Rough Sets and Soft Computing, San Jose, pp. 490–497 (1994)

    Google Scholar 

  19. Łuba, T., Lasocki, R., Rybnik, J.: An implementation of decomposition algorithm and its application in information systems analysis and logic synthesis. In: Ziarko, W. (ed.) Rough Sets, Fuzzy Sets and Knowledge Discovery. Workshops in Computing Series, pp. 458–465. Springer (1994)

    Google Scholar 

  20. Łuba, T., Rybnik, J.: Rough sets and some aspects in logic synthesis. In: Słowiński, R. (ed.) Intelligent Decision Support – Handbook of Application and Advances of the Rough Sets Theory. Kluwer Academic Publishers (1992)

    Google Scholar 

  21. Nguyen, D., Nguyen, X.: A new method to attribute reduction of decision systems with covering rough sets. Georgian Electronic Scientific Journal: Computer Science and Telecommunications 1(24), 24–31 (2010)

    Google Scholar 

  22. Pawlak, Z.: Rough Sets. Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishers (1991)

    Google Scholar 

  23. Pei, X., Wang, Y.: An approximate approach to attribute reduction. International Journal of Information Technology 12(4), 128–135 (2006)

    Google Scholar 

  24. Rawski, M., Borowik, G., Łuba, T., Tomaszewicz, P., Falkowski, B.J.: Logic synthesis strategy for FPGAs with embedded memory blocks. Electrical Review 86(11a), 94–101 (2010)

    Google Scholar 

  25. Selvaraj, H., Sapiecha, P., Rawski, M., Łuba, T.: Functional decomposition – the value and implication for both neural networks and digital designing. International Journal of Computational Intelligence and Applications 6(1), 123–138 (2006), doi:10.1142/S1469026806001782

    Article  Google Scholar 

  26. Skowron, A., Rauszer, C.: The discernibility matrices and functions in information systems. In: Słowiński, R. (ed.) Intelligent Decision Support – Handbook of Application and Advances of the Rough Sets Theory. Kluwer Academic Publishers (1992)

    Google Scholar 

  27. Słowiński, R., Sharif, E.: Rough sets analysis of experience in surgical practice. In: Rough Sets: State of The Art and Perspectives, Poznań-Kiekrz (1992)

    Google Scholar 

  28. Wang, C., Ou, F.: An attribute reduction algorithm based on conditional entropy and frequency of attributes. In: Proceedings of the 2008 International Conference on Intelligent Computation Technology and Automation, ICICTA 2008, vol. 1, pp. 752–756. IEEE Computer Society, Washington, DC (2008), doi:10.1109/ICICTA.2008.95

    Chapter  Google Scholar 

  29. Yao, Y., Zhao, Y.: Attribute reduction in decision-theoretic rough set models. Information Sciences 178(17), 3356–3373 (2008), doi:10.1016/j.ins.2008.05.010

    Article  MathSciNet  MATH  Google Scholar 

  30. ROSE2 – Rough Sets Data Explorer, http://idss.cs.put.poznan.pl/site/rose.html

  31. ROSETTA – A Rough Set Toolkit for Analysis of Data, http://www.lcb.uu.se/tools/rosetta/

  32. RSES – Rough Set Exploration System, http://logic.mimuw.edu.pl/~rses/

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Grzegorz Borowik .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Borowik, G., Łuba, T. (2014). Fast Algorithm of Attribute Reduction Based on the Complementation of Boolean Function. In: Klempous, R., Nikodem, J., Jacak, W., Chaczko, Z. (eds) Advanced Methods and Applications in Computational Intelligence. Topics in Intelligent Engineering and Informatics, vol 6. Springer, Heidelberg. https://doi.org/10.1007/978-3-319-01436-4_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-01436-4_2

  • Publisher Name: Springer, Heidelberg

  • Print ISBN: 978-3-319-01435-7

  • Online ISBN: 978-3-319-01436-4

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics