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Rough Sets and Artificial Neural Networks

  • Marcin S. Szczuka
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 19)

Abstract

This work is an attempt to summarize several approaches aimed at connecting Rough Set Theory with Artificial Neural Networks. Both methodologies have their place among intelligent classification and decision support methods. Artificial Neural Networks belong to most commonly used techniques in applications of Artificial Intelligence. During the last twenty years of its development numerous theoretical and applied works have been done in that field. Rough Set Theory which emerged about fifteen years ago is nowadays rapidly developing branch of AI and Soft Computing.

Keywords

Neural Network Hide Layer Decision Rule Decision Table Decision Class 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Banerjee, M., Mitra, S., Pal, S.K. Rough fuzzy MLP: Knowledge encoding and classification. IEEE Transactions on Neural Networks(1997) (submitted)Google Scholar
  2. 2.
    Hecht-Nielsen, R.: Neurocomputing. Addison-Wesley, New York (1990)Google Scholar
  3. 3.
    Jelonek, J., Krawiec, K., Slowinski, R.: Rough set reduction of attributes and their domains for neural networks, Computational Intelligence 11 /2 (1995) 339–347CrossRefGoogle Scholar
  4. 4.
    Jelonek, J., Krawiec, K., Slowinski, R., Stefanowski, J., Szymas, J.: Rough sets as an intelligent front-end for the neural network. In: Proceedings of First National Conference “Neural Networks and their Applications”, April 12–15, Kule (1994) 268–273Google Scholar
  5. 5.
    Karayiannis, N.B., Venetsanopoulos, A.N.: Artificial neural networks: Learning algorithms, performance evaluation and applications. Kluwer, Dortrecht (1993)Google Scholar
  6. 6.
    Kohavi, R., Sahami, M.: Error—based and entropy—based discretization of continuous features. In: E. Simoudis, J. Han, and U.M. Fayyad (eds.): Proc. of the Second International Conference on Knowledge Discovery & Data Mining. Portland, Oregon (1996) 114–119Google Scholar
  7. 7.
    Kruse, R., Gebhardt, J., Klawonn F.: Foundations of fuzzy systems. Wiley, Chichester (1994)Google Scholar
  8. 8.
    Lingras, P.: Rough neural networks. In: Proceedings of the Sixth International Conference, Information Procesing and Management of Uncertainty in Knowledge-Based Systems (IPMU’96), July 1–5, Granada, Spain (1996) 3 1445–1450Google Scholar
  9. 9.
    Lingras, P.: Comparison of neofuzzy and rough neural networks. In: P.P. Wang (ed.): Proceedings of the Fifth International Workshop on Rough Sets and Soft Computing (RSSC’97) at Third Annual Joint Conference on Information Sciences (JCIS’97), Duke University, Durham, NC, USA, Rough Set & Computer Science 3, March 1–5 (1997) 259–262Google Scholar
  10. 10.
    Machine learning databases, University of California, Irvine. ftp://ics.uci.edu/machine-learning-databasesGoogle Scholar
  11. 11.
    Michalski, R.S., Mozetic, I., Hong, J., Lavrac, N.: The multi—purpose incremental learning system AQ15 and its testing applications to three medical domains. In: Proc. of 5 National Conference on Artificial Intelligence, Philadelphia, Morgan-Kaufman, (1986) 1041–1045Google Scholar
  12. 12.
    Michalewicz, Z.: Genetic algorithms + data structures = evolution programs. Springer—Verlag, Berlin (1992)Google Scholar
  13. 13.
    Nguyen, H.Son, Nguyen, S. Hoa: From optimal hyperplanes to optimal decision tree. In: S. Tsumoto, S. Kobayashi, T. Yokomori, H. Tanaka, and A. Nakamura (eds.): Proceedings of the Fourth International Workshop on Rough Sets, Fuzzy Sets, and Machine Discovery (RSFD’96), The University of Tokyo, November 6–8 (1996) 82–88Google Scholar
  14. 14.
    Nguyen, H. Son., Nguyen, S. Hoa, Skowron, A.: Searching for features defined by hyperplanes. In: In: Z.W. Ras, M. Michalewicz (eds.), Ninth International Symposium on Methodologies for Intelligent Systems. Zakopane, Poland, June 9–13, Lecture Notes in Artificial Intelligence (ISMIS’96) 1079, Springer—Verlag, Berlin (1996) 366–375Google Scholar
  15. 15.
    Nguyen, H.Son, Skowron, A.: Quantization of real-valued attributes. Rough Set and Boolean Reasoning Approaches. In: P.P. Wang (ed.): Second Annual Joint Conference on Information Sciences (JCIS’95), Wrightsville Beach, North Carolina, 28 September–1 October (1995) 34–37Google Scholar
  16. 16.
    Nguyen, H. Son, Szczuka, M., Slgzak, D.: Neural networks design: Rough set approach to real-valued data. In: J. Komorowski, J. Zytkow, (eds.), The First European Symposium on Principle of Data Mining and Knowledge Discovery (PKDD’97), June 25–27, Trondheim, Norway, Lecture Notes in Artificial Intelligence 1263, Springer-Verlag, Berlin (1997) 359–366Google Scholar
  17. 17.
    Sapiecha, P.: An approximation algorithm for certain class of NP-hard problems. In: ICS Research Report 21/92 Warsaw University of Technology (1992)Google Scholar
  18. 18.
    Skowron, A., Rauszer, C.: The discernibility matrices and functions in information systems. In: R. Slowinski (ed.): Intelligent Decision Support — Handbook of Applications and Advances of the Rough Sets Theory, Kluwer Academic Publishers, Dordrecht (1992) 331–362CrossRefGoogle Scholar
  19. 19.
    Szczuka, M., Slgzak, D.: Hyperplane-based neural networks for real-valued decision tables. In: P.P. Wang (ed.): Proceedings of the Fifth International Workshop on Rough Sets and Soft Computing (RSSC’97) at Third Annual Joint Conference on Information Sciences (JCIS’97), Duke University, Durham, NC, USA, Rough Set & Computer Science 3, March 1–5 (1997) 265–268Google Scholar
  20. 20.
    Szczuka, M.: Aproksymacja funkcji za pomocg, sieci neuronowych z wykorzystaniem metod zbiorów przybliionych. Master Thesis, Faculty of Mathematics, Informatics and Mechanics, The University of Warsaw (1995)Google Scholar
  21. 21.
    Szczuka, M.: Rough set methods for constructing artificial neural networks. In: B.D. Czejdo, I.I. Est, B. Shirazi, B. Trousse (eds.), Proceedings of the Third Biennial European Joint Conference on Engineering Systems Design and Analysis 7, July 1–4, Montpellier, France (1996) 9–14Google Scholar
  22. 22.
    Swiniarski, R., Berzins, A.: Rouh sets expert system for on-line prediction of volleyball game progress. In: B.D. Czejdo, I.I. Est, B. Shirazi, B. Trousse (eds.), Proceedings of the Third Biennial European Joint Conference on Engineering Systems Design and Analysis 7, July 1–4, Montpellier, France (1996) 3–8Google Scholar
  23. 23.
    winiarski, R., Hunt, F., Chalvet, D., Pearson, D.: Prediction system based on neural networks and rough sets in a highly automated production process. In: Proceedings of the 12th System Science Conference, Wroclaw, Poland (1995)Google Scholar
  24. 24.
    Swiniarski, R., Hunt, F., Chalvet, D., Pearson, D.: Intelligent data processing and dynamic process discovery using rough sets, statistical reasoning and neural networks in a highly automated production systems. In: Proceedings of the First European Conference on Application of Neural Networks in Industry, Helsinki, Finland (1995)Google Scholar
  25. 25.
    Vapnik, V.N.: The nature of statistical learning theory. Springer-Verlag, New York (1995)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Marcin S. Szczuka
    • 1
  1. 1.Institute of MathematicsWarsaw UniversityWarsawPoland

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