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Hierarchical Rough Classifiers

  • Conference paper
Rough Sets and Intelligent Systems Paradigms (RSEISP 2007)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4585))

Abstract

The major applications of rough set theory in data mining are related to the modeling of concepts using rough classifiers, i.e., the algorithms classifying unseen objects into lower or upper approximations of concepts. This paper investigates a class of compound classifiers called multi-level (or hierarchical) rough classifiers (MLRC). We present the most recent issues on the construction of such classifiers from data using concept ontology as an additional domain knowledge. The idea is based on the bottom-up manner to gradually synthesize the multi-layer rough classifier for the complex target concept from the simpler classifiers.We illustrate the proposed method by experiments on real-life data.

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References

  1. Kloesgen, W., Żytkow, J. (eds.): Handbook of Knowledge Discovery and Data Mining. Oxford University Press, Oxford (2002)

    MATH  Google Scholar 

  2. Pawlak, Z.: Rough sets. International Journal of Computer and Information Sciences 11, 341–356 (1982)

    Article  MATH  MathSciNet  Google Scholar 

  3. Pawlak, Z.: Some Issues on Rough Sets. Transactions on Rough Sets 1, 1–58 (2004)

    MathSciNet  Google Scholar 

  4. Nguyen, S.H., Bazan, J., Skowron, A., Nguyen, H.S.: Layered Learning for Concept synthesis. In: Peters, J.F., Skowron, A., Grzymala-Busse, J.W., Kostek, B, Świniarski, R.W., Szczuka, M. (eds.) Transactions on Rough Sets I. LNCS, vol. 3100, pp. 187–208. Springer, Heidelberg (2004)

    Google Scholar 

  5. Nguyen, T.T., Willis, C.P., Paddon, D.J., Nguyen, S.H., Nguyen, H.S.: Learning sunspot classification. Fundamenta Informaticea 72(1-3), 295–309 (2006)

    MATH  MathSciNet  Google Scholar 

  6. Stone, P.: Layered Learning in Multi-Agent Systems: A Winning Approach to Robotic Soccer. The MIT Press, Cambridge, MA (2000)

    Google Scholar 

  7. Ziarko, W.: Variable Precision Rough Set Model. Journal of Computer and System Sciences 46, 39–59 (1993)

    Article  MATH  MathSciNet  Google Scholar 

  8. Skowron, A.: Approximation spaces in rough neurocomputing. In: Inuiguchi, M., Tsumoto, S., Hirano, S. (eds.) Rough Set Theory and Granular Computing. Studies in Fuzziness and Soft Computing, vol. 125, pp. 13–22. Springer, Heidelberg, Germany (2003)

    Google Scholar 

  9. Nguyen, S.H., Nguyen, T.T., Nguyen, H.S.: Ontology driven concept approximation. In: Greco, S., Hata, Y., Hirano, S., Inuiguchi, M., Miyamoto, S., Nguyen, H.S., Slowinski, R. (eds.) RSCTC 2006. LNCS, vol. 4259, pp. 547–556. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  10. Skowron, A., Stepaniuk, J.: Information Granules and Rough-Neural Computing [13] pp. 43–84

    Google Scholar 

  11. Zadeh, L.A.: Fuzzy logic = computing with words. IEEE Transactions on Fuzzy Systems 4, 103–111 (1996)

    Article  Google Scholar 

  12. Zadeh, L.A.: A new direction in AI: Toward a computational theory of perceptions. AI Magazine 22(1), 73–84 (2001)

    Google Scholar 

  13. Pal, S.K., Polkowski, L., Skowron, A. (eds.): Rough-Neural Computing: Techniques for Computing with Words. Cognitive Technologies. Springer, Heidelberg (2003)

    Google Scholar 

  14. Polkowski, L., Skowron, A.: Rough Mereology: A New Paradigm for Approximate Reasoning. International Journal of Approximate Reasoning 15(4), 333–365 (1996)

    Article  MATH  MathSciNet  Google Scholar 

  15. Polkowski, L., Skowron, A.: Towards Adaptive Calculus of Granules. In: Zadeh, L.A., Kacprzyk, J. (eds.) Computing with Words in Information/Intelligent Systems, pp. 201–227. Springer, Heidelberg (1999)

    Google Scholar 

  16. Polkowski, L., Skowron, A.: Rough mereological calculi of granules: A rough set approach to computation. Computational Intelligence 17(3), 472–492 (2001)

    Article  MathSciNet  Google Scholar 

  17. Skowron, A., Stepaniuk, J.: Information granules: Towards foundations of granular computing. International Journal of Intelligent Systems 16(1), 57–86 (2001)

    Article  MATH  Google Scholar 

  18. Friedman, J.H., Hastie, T., Tibshirani, R.: The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer, Heidelberg, Germany (2001)

    MATH  Google Scholar 

  19. Mitchell, T.: Machine Learning. Mc Graw Hill, New York (1998)

    Google Scholar 

  20. Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers Inc, San Francisco, CA (2000)

    Google Scholar 

  21. Nguyen, H.S., Nguyen, S.H.: Fast split selection method and its application in decision tree construction from large databases. International Journal of Hybrid Intelligent Systems 2(2), 149–160 (2005)

    MATH  Google Scholar 

  22. Nguyen, H.S.: Approximate Boolean Reasoning: Foundations and Applications in Data Mining. In: Peters, J.F., Skowron, A. (eds.) Transactions on Rough Sets V. Lecture Notes on Computer Science, vol. 4100, pp. 334–506. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

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Author information

Authors and Affiliations

  1. Polish-Japanese Institute of Information Technology, Koszykowa 86, 02008, Warszawa, Poland

    Sinh Hoa Nguyen

  2. Institute of Mathematics, Warsaw University, Banacha 2, 02-097 Warsaw, Poland

    Hung Son Nguyen

Authors
  1. Sinh Hoa Nguyen
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  2. Hung Son Nguyen
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Editor information

Marzena Kryszkiewicz James F. Peters Henryk Rybinski Andrzej Skowron

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© 2007 Springer-Verlag Berlin Heidelberg

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Nguyen, S.H., Nguyen, H.S. (2007). Hierarchical Rough Classifiers. In: Kryszkiewicz, M., Peters, J.F., Rybinski, H., Skowron, A. (eds) Rough Sets and Intelligent Systems Paradigms. RSEISP 2007. Lecture Notes in Computer Science(), vol 4585. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73451-2_6

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  • DOI: https://doi.org/10.1007/978-3-540-73451-2_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-73450-5

  • Online ISBN: 978-3-540-73451-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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