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Layered Learning for Concept Synthesis

  • Conference paper
Transactions on Rough Sets I

Part of the book series: Lecture Notes in Computer Science ((TRS,volume 3100))

Abstract

We present a hierarchical scheme for synthesis of concept approximations based on given data and domain knowledge. We also propose a solution, founded on rough set theory, to the problem of constructing the approximation of higher level concepts by composing the approximation of lower level concepts. We examine the effectiveness of the layered learning approach by comparing it with the standard learning approach. Experiments are carried out on artificial data sets generated by a road traffic simulator.

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References

  1. Aha, D.W.: The omnipresence of case-based reasoning in science and application. Knowledge-Based Systems 11(5-6), 261–273 (1998)

    Article  Google Scholar 

  2. Barwise, J., Seligman, J. (eds.): Information Flow: The Logic of Distributed Systems. Tracts in Theoretical Computer Science, vol. 44. Cambridge University Press, Cambridge (1997)

    MATH  Google Scholar 

  3. Bazan, J.G.: A comparison of dynamic and non-dynamic rough set methods for extracting laws from decision tables. In: Polkowski, L., Skowron, A. (eds.) Rough Sets in Knowledge Discovery 1: Methodology and Applications, pp. 321–365. Physica-Verlag, Heidelberg (1998)

    Google Scholar 

  4. Bazan, J.G., Szczuka, M.: RSES and RSESlib - a collection of tools for rough set computations. In: Ziarko, W.P., Yao, Y. (eds.) RSCTC 2000. LNCS (LNAI), vol. 2005, pp. 106–113. Springer, Heidelberg (2000)

    Chapter  Google Scholar 

  5. Bazan, J., Nguyen, H.S., Skowron, A., Szczuka, M.: A view on rough set concept approximation. In: Wang, G., Liu, Q., Yao, Y., Skowron, A. (eds.) RSFDGrC 2003. LNCS (LNAI), vol. 2639, pp. 181–188. Springer, Heidelberg (2003)

    Google Scholar 

  6. Cover, T.M., Hart, P.E.: Nearest neighbor pattern classification. IEEE Transactions on Information Theory 13, 21–27 (1967)

    Article  MATH  Google Scholar 

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

    MATH  Google Scholar 

  8. Grzymala-Busse, J.: A new version of the rule induction system lers. Fundamenta Informaticae 31(1), 27–39 (1997)

    MathSciNet  MATH  Google Scholar 

  9. Komorowski, J., Pawlak, Z., Polkowski, L., Skowron, A.: Rough sets: a tutorial. In: Pal, S.K., Skowron, A. (eds.) Rough Fuzzy Hybridization: A New Trend in Decision-Making, pp. 3–98. Springer, Singapore (1999)

    Google Scholar 

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

    MATH  Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  13. Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning about Data. In: System Theory, Knowledge Engineering and Problem Solving, vol. 9. Kluwer Academic Publishers, Dordrecht (1991)

    Google Scholar 

  14. Poggio, T., Smale, S.: The mathematics of learning: Dealing with data. Notices of the AMS 50, 537–544 (2003)

    MathSciNet  MATH  Google Scholar 

  15. Polkowski, L., Skowron, A.: Rough mereology: A new paradigm for approximate reasoning. International Journal of Approximate Reasoning 15, 333–365 (1996)

    Article  MathSciNet  MATH  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  17. 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. Physica-Verlag, Heidelberg (1999)

    Google Scholar 

  18. Skowron, A., Stepaniuk, J.: Information granules and rough-neural computing. In: [12], pp. 43–84

    Google Scholar 

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

    Article  MATH  Google Scholar 

  20. Skowron, A., Stepaniuk, J.: Information granule decomposition. Fundamenta In-formaticae 47(3-4), 337–350 (2001)

    MathSciNet  MATH  Google Scholar 

  21. Skowron, A.: Approximate reasoning by agents in distributed environments. In: Zhong, N., Liu, J., Ohsuga, S., Bradshaw, J. (eds.) Intelligent Agent Technology Research and Development: Proceedings of the 2nd Asia-Pacific Conference on Intelligent Agent Technology IAT01, Maebashi, Japan, October 23-26, pp. 28–39. World Scientific, Singapore (2001)

    Chapter  Google Scholar 

  22. 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 (2003)

    Google Scholar 

  23. Skowron, A., Rauszer, C.: The discernibility matrices and functions in information systems. In: Slowinski, R. (ed.) Intelligent Decision Support - Handbook of Applications and Advances of the Rough Sets Theory. D: System Theory, Knowledge Engineering and Problem Solving, vol. 11, pp. 331–362. Kluwer Academic Publishers, Dordrecht (1992)

    Google Scholar 

  24. Skowron, A., Szczuka, M.: Approximate reasoning schemes: Classifiers for computing with words. In: Proceedings of SMPS 2002, Advances in Soft Computing, pp. 338–345. Springer, Heidelberg (2002)

    Google Scholar 

  25. Stone, P.: Layered Learning in Multi-Agent Systems: A Winning Approach toRobotic Soccer. The MIT Press, Cambridge (2000)

    Google Scholar 

  26. Wróblewski, J.: Covering with reducts - a fast algorithm for rule generation. In: Polkowski, L., Skowron, A. (eds.) RSCTC 1998. LNCS (LNAI), vol. 1424, pp. 402–407. Springer, Heidelberg (1998)

    Chapter  Google Scholar 

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

    Article  Google Scholar 

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

    Google Scholar 

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© 2004 Springer-Verlag Berlin Heidelberg

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Nguyen, S.H., Bazan, J., Skowron, A., Nguyen, H.S. (2004). Layered Learning for Concept Synthesis. In: Peters, J.F., Skowron, A., Grzymała-Busse, J.W., Kostek, B., Świniarski, R.W., Szczuka, M.S. (eds) Transactions on Rough Sets I. Lecture Notes in Computer Science, vol 3100. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-27794-1_9

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  • DOI: https://doi.org/10.1007/978-3-540-27794-1_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22374-0

  • Online ISBN: 978-3-540-27794-1

  • eBook Packages: Springer Book Archive

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