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Classifiers Based on Data Sets and Domain Knowledge: A Rough Set Approach

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Rough Sets and Intelligent Systems - Professor Zdzisław Pawlak in Memoriam

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 43))

Abstract

The problem considered is how to construct classifiers for approximation of complex concepts on the basis of experimental data sets and domain knowledge that are mainly represented by concept ontology. The approach presented in this chapter to solving this problem is based on the rough set theory methods. Rough set theory introduced by Zdzisław Pawlak during the early 1980s provides the foundation for the construction of classifiers. This approach is applied to approximate spatial complex concepts and spatio-temporal complex concepts defined for complex objects, to identify the behavioral patterns of complex objects, and to the automated behavior planning for such objects when the states of objects are represented by spatio-temporal concepts requiring approximation. The chapter includes results of experiments that have been performed on data from a vehicular traffic simulator and the recent results of experiments that have been performed on medical data sets obtained from Second Department of Internal Medicine, Jagiellonian University Medical College, Cracow, Poland. Moreover, we also describe the results of experiments that have been performed on medical data obtained from Neonatal Intensive Care Unit in the Department of Pediatrics, Jagiellonian University Medical College, Cracow, Poland.

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References

  1. van der Aalst, W.M.P.: Process Mining: Discovery, Conformance and Enhancement of Business Processes. Springer, Berlin (2011)

    MATH  Google Scholar 

  2. Altman, D.G.: Practical Statistics for Medical Research. Chapman and Hall/CRC, London (1997)

    Google Scholar 

  3. Bar-Yam, Y.: Dynamics of Complex Systems. Addison-Wesley, New York (1997)

    MATH  Google Scholar 

  4. Bazan, J.G.: Hierarchical Classifiers for Complex Spatio-temporal Concepts. In: Peters, J.F., Skowron, A., Rybiński, H. (eds.) Transactions on Rough Sets IX. LNCS, vol. 5390, pp. 474–750. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  5. Bazan, J.G.: Rough sets and granular computing in behavioral pattern identification and planning. In: Pedrycz, W., Skowron, A., Kreinovich, V. (eds.) Handbook of Granular Computing, pp. 777–799. John Wiley & Sons, The Atrium (2008)

    Google Scholar 

  6. Bazan, J.G., Kruczek, P., Bazan-Socha, S., Skowron, A., Pietrzyk, J.: Automatic planning based on rough set tools: Towards supporting treatment of infants with respiratory failure. In: Proceedings of the Workshop on Concurrency, Specification, and Programming (CS&P 2006), Wandlitz, Germany, September 27-29. Informatik-Bericht, vol. 170, pp. 388–399. Humboldt University, Berlin (2006)

    Google Scholar 

  7. Bazan, J.G., Kruczek, P., Bazan-Socha, S., Skowron, A., Pietrzyk, J.: Automatic Planning of Treatment of Infants with Respiratory Failure Through Rough Set Modeling. In: Greco, S., Hata, Y., Hirano, S., Inuiguchi, M., Miyamoto, S., Nguyen, H.S., Słowiński, R. (eds.) RSCTC 2006. LNCS (LNAI), vol. 4259, pp. 418–427. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  8. Bazan, J.G., Kruczek, P., Bazan-Socha, S., Skowron, A., Pietrzyk, J.: Risk pattern identification in the treatment of infants with respiratory failure through rough set modeling. In: Proceedings of the Eleventh Conference of Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU 2006), Paris, France, July 2-7, pp. 2650–2657 (2006)

    Google Scholar 

  9. Bazan, J.G., Kruczek, P., Bazan-Socha, S., Skowron, A., Pietrzyk, J.: Rough set approach to behavioral pattern identification. Fundamenta Informaticae 75(1-4), 27–47 (2007)

    MathSciNet  Google Scholar 

  10. Bazan, J.G., Skowron, A.: Classifiers based on approximate reasoning schemes. In: Dunin-Kęplicz, B., Jankowski, A., Skowron, A., Szczuka, M. (eds.) Monitoring, Security, and Rescue Techniques in Multiagent Systems. Advances in Soft Computing, pp. 191–202. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  11. Borrett, S.R., Bridewell, W., Langley, P., Arrigo, K.R.: A method for representing and developing process models. Ecological Complexity 4, 1–12 (2007)

    Article  Google Scholar 

  12. Breiman, L.: Statistical modeling: the two cultures. Statistical Science 16(3), 199–231 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  13. Desai, A.: Adaptive complex enterprises. Communications ACM 5(48), 32–35 (2005)

    Article  Google Scholar 

  14. Doherty, P., Łukaszewicz, W., Skowron, A., Szałas, A.: Knowledge Engineering: A Rough Set Approach. Springer, Heidelberg (2006)

    MATH  Google Scholar 

  15. Domingos, P.: Toward knowledge-rich data mining. Data Mining and Knowledge Discovery 1(15), 21–28 (2007)

    Article  MathSciNet  Google Scholar 

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

    MATH  Google Scholar 

  17. Guarino, N.: Formal ontology and information systems. In: Proceedings of the First International Conference on Formal Ontology in Information Systems (FOIS 1998), Trento, Italy, June 6-8, pp. 3–15. IOS Press (1998)

    Google Scholar 

  18. Hillerbrand, R., Sandin, P., Peterson, M., Roeser, S. (eds.): Handbook of Risk Theory: Epistemology, Decision Theory, Ethics, and Social Implications of Risk. Springer, Berlin (2012)

    Google Scholar 

  19. Hoen, P.J., Tuyls, K., Panait, L., Luke, S., Poutré, J.A.L.: An Overview of Cooperative and Competitive Multiagent Learning. In: Tuyls, K., ’t Hoen, P.J., Verbeeck, K., Sen, S. (eds.) LAMAS 2005. LNCS (LNAI), vol. 3898, pp. 1–46. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  20. Ignizio, J.P.: An Introduction to Expert Systems. McGraw-Hill, New York (1991)

    Google Scholar 

  21. Jarrar, M.: Towards Methodological Principles for Ontology Engineering. Ph.D. thesis, Vrije Universiteit Brussel (2005)

    Google Scholar 

  22. Keefe, R.: Theories of Vagueness. Cambridge University Press, New York (2000)

    Google Scholar 

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

    MATH  Google Scholar 

  24. Kriegel, H.P., Borgwardt, K.M., Kröger, P., Pryakhin, A., Schubert, M., Zimek, A.: Future trends in data mining. Data Mining and Knowledge Discovery 1(15), 87–97 (2007)

    Article  Google Scholar 

  25. Langley, P.: Cognitive architectures and general intelligent systems. AI Magazine 27, 33–44 (2006)

    Google Scholar 

  26. Liu, J., Jin, X., Tsui, K.: Autonomy Oriented Computing: From Problem Solving to Complex Systems Modeling. Kluwer/Springer, Heidelberg (2005)

    MATH  Google Scholar 

  27. Luck, M., McBurney, P., Shehory, O., Willmott, S.: Agent technology: Computing as interaction. a roadmap for agent-based computing. Agentlink iii, the european coordination action for agent-based computing. University of Southampton, UK (2005)

    Google Scholar 

  28. Michalski, R., et al. (eds.): Machine Learning, vol. I-IV. Morgan Kaufmann, Los Altos (1983, 1986, 1990, 1994)

    Google Scholar 

  29. Michie, D., Spiegelhalter, D.J., Taylor, C.C.: Machine learning, neural and statistical classification. Ellis Horwood Limited, England (1994)

    MATH  Google Scholar 

  30. Mitchel, T.M.: Machine Learning. McGraw-Hill, Boston (1997)

    Google Scholar 

  31. Pancerz, K., Suraj, Z.: Rough sets for discovering concurrent system models from data tables. In: Hassanien, A.E., Suraj, Z., Ślęzak, D., Lingras, P. (eds.) Rough Computing: Theories, Technologies and Applications. Idea Group, Inc. (2007)

    Google Scholar 

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

    Google Scholar 

  33. Pawlak, Z., Skowron, A.: Rudiments of rough sets. Information Sciences 177, 3–27 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  34. Peters, J.F.: Rough Ethology: Towards a Biologically-Inspired Study of Collective Behavior in Intelligent Systems with Approximation Spaces. In: Peters, J.F., Skowron, A. (eds.) Transactions on Rough Sets III. LNCS, vol. 3400, pp. 153–174. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  35. Peters, J.F., Skowron, A.: Zdzisław Pawlak life and work (1926–2006). Information Sciences 177, 1–2 (2007)

    Article  MathSciNet  Google Scholar 

  36. Bazan, J.G., Szczuka, M.S.: The Rough Set Exploration System. In: Peters, J.F., Skowron, A. (eds.) Transactions on Rough Sets III. LNCS, vol. 3400, pp. 37–56. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  37. Peters, J.F., Skowron, A., Rybiński, H. (eds.): Transactions on Rough Sets IX. LNCS, vol. 5390. Springer, Heidelberg (2008)

    MATH  Google Scholar 

  38. Poggio, T., Smale, S.: The mathematics of learning: Dealing with data. Notices of the American Mathematical Society (AMS) 5(50), 537–544 (2003)

    MathSciNet  Google Scholar 

  39. Polkowski, L., Skowron, A.: Rough Mereology. In: Raś, Z.W., Zemankova, M. (eds.) ISMIS 1994. LNCS, vol. 869, pp. 85–94. Springer, Heidelberg (1994)

    Chapter  Google Scholar 

  40. Read, S.: Thinking about Logic: An Introduction to the Philosophy of Logic. Oxford University Press, New York (1994)

    Google Scholar 

  41. Ripley, B.D.: Pattern Recognition and Neural Networks. Cambridge University Press, Cambridge (1996)

    MATH  Google Scholar 

  42. Rough ICE: Project web site, http://logic.mimuw.edu.pl/~bazan/roughice/

  43. RSES: Project web site, http://logic.mimuw.edu.pl/~rses

  44. Simulator: Project web site, http://logic.mimuw.edu.pl/~bazan/simulator

  45. Skowron, A., Stepaniuk, J., Swiniarski, R.W.: Modeling rough granular computing based on approximation spaces. Information Sciences 184(1), 20–43 (2012)

    Article  Google Scholar 

  46. Stone, P., Sridharan, M., Stronger, D., Kuhlmann, G., Kohl, N., Fidelman, P., Jong, N.K.: From pixels to multi-robot decision-making: A study in uncertainty. Robotics and Autonomous Systems 54(11), 933–943 (2006)

    Article  Google Scholar 

  47. Suraj, Z.: Discovering concurrent data models and decision algorithms from data: A rough set approach. International Journal on Artificial Intelligence and Machine Learning, IRSI, 51–56 (2004)

    Google Scholar 

  48. The Infobright Community Edition (ICE) Homepage at, http://www.infobright.org/

  49. Unnikrishnan, K.P., Ramakrishnan, N., Sastry, P.S., Uthurusamy, R.: Service-oriented science: Scaling escience impact. In: Proceedings of the Fourth KDD Workshop on Temporal Data Mining: Network Reconstruction from Dynamic Data, The Twelfth ACM SIGKDD International Conference on Knowledge Discovery and Data (KDD 2006), Philadelphia, USA, August 20-23 (2006)

    Google Scholar 

  50. Urmson, C., et al.: High speed navigation of unrehearsed terrain: Red team technology for grand challenge. Report CMU-RI-TR-04-37, The Robotics Institute, Carnegie Mellon University (2004)

    Google Scholar 

  51. Vapnik, V. (ed.): Statistical Learning Theory. Wiley, New York (1998)

    MATH  Google Scholar 

  52. Zadeh, L.A.: From computing with numbers to computing with words – from manipulation of measurements to manipulation of perceptions. IEEE Transactions on Circuits and Systems – I: Fundamental Theory and Applications 1(45), 105–119 (1999)

    Article  MathSciNet  Google Scholar 

  53. Zadeh, L.A.: Toward a generalized theory of uncertainty (GTU) - an outline. Information Sciences 171, 1–40 (2005)

    Article  MathSciNet  Google Scholar 

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Authors and Affiliations

  1. Institute of Computer Science, University of Rzeszów, Dekerta 2, 35 - 030, Rzeszów, Poland

    Jan G. Bazan, Sylwia Buregwa-Czuma & Przemysław Wiktor Pardel

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

    Jan G. Bazan, Przemysław Wiktor Pardel & Andrzej Skowron

  3. Second Department of Internal Medicine, Jagiellonian University Medical College, Skawinska 8, 31-066, Cracow, Poland

    Stanisława Bazan-Socha & Barbara Sokołowska

Authors
  1. Jan G. Bazan
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  2. Stanisława Bazan-Socha
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  4. Przemysław Wiktor Pardel
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  5. Andrzej Skowron
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  6. Barbara Sokołowska
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Correspondence to Jan G. Bazan .

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Editors and Affiliations

  1. Institute of Mathematics, Warsaw University, Banacha 2, Warsaw, 02097, Poland

    Andrzej Skowron

  2. Institute of Computer Science, University of Rzeszów, ul. Dekerta 2, Rzeszów, 35-030, Poland

    Zbigniew Suraj

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Bazan, J.G., Bazan-Socha, S., Buregwa-Czuma, S., Pardel, P.W., Skowron, A., Sokołowska, B. (2013). Classifiers Based on Data Sets and Domain Knowledge: A Rough Set Approach. In: Skowron, A., Suraj, Z. (eds) Rough Sets and Intelligent Systems - Professor Zdzisław Pawlak in Memoriam. Intelligent Systems Reference Library, vol 43. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30341-8_7

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  • DOI: https://doi.org/10.1007/978-3-642-30341-8_7

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