Skip to main content

Decision Rule-Based Data Models Using TRS and NetTRS – Methods and Algorithms

  • Chapter
Transactions on Rough Sets XI

Part of the book series: Lecture Notes in Computer Science ((TRS,volume 5946))

Abstract

The internet service NetTRS (Network TRS) that enable to realize induction, evaluation, and postprocessing of decision rules is presented in the paper. The TRS (Tolerance Rough Sets) library is the main part of the service. The TRS library makes possible to induct, generalize and filtrate decision rules. Moreover, TRS enables to evaluate rules and conduct the classification process. The NetTRS service is a package of the library in user interface and makes it accessible in the Internet. NetTRS put principal emphasis on induction and postprocessing of decision rules, the paper describes methods and algorithms that are available in the service.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight 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

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Agotnes, T.: Filtering large propositional rule sets while retaining classifier performance. MSc Thesis. Norwegian University of Science and Technology, Trondheim, Norway (1999)

    Google Scholar 

  2. Agotnes, T., Komorowski, J., Loken, T.: Taming Large Rule Models in Rough Set Approaches. In: Żytkow, J.M., Rauch, J. (eds.) PKDD 1999. LNCS (LNAI), vol. 1704, pp. 193–203. Springer, Heidelberg (1999)

    Google Scholar 

  3. An, A., Cercone, N.: Rule quality measures for rule induction systems – description and evaluation. Computational Intelligence 17, 409–424 (2001)

    Article  Google Scholar 

  4. Bazan, J., Skowron, A., Wang, H., Wojna, A.: Multimodal classification: case studies. In: Peters, J.F., Skowron, A. (eds.) Transactions on Rough Sets V. LNCS, vol. 4100, pp. 224–239. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  5. Bazan, J.: A comprasion 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: Methododology and Applications, pp. 321–365. Physica, Heidelberg (1998)

    Google Scholar 

  6. Bazan, J., Szczuka, M., Wróblewski, J.: A new version of rough set exploration system. In: Alpigini, J.J., Peters, J.F., Skowron, A., Zhong, N. (eds.) RSCTC 2002. LNCS (LNAI), vol. 2475, pp. 397–404. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  7. Breiman, L., Friedman, J., Olshen, R., Stone, R.: Classificzation and Regression Trees. Wadsworth, Pacific Grove (1984)

    Google Scholar 

  8. Brazdil, P.B., Togo, L.: Knowledge acquisition via knowledge integration. Current Trends in Knowledge Acquisition. IOS Press, Amsterdam (1990)

    Google Scholar 

  9. Bruha, I.: Quality of Decision Rules: Definitions and Classification Schemes for Multiple Rules. In: Nakhaeizadeh, G., Taylor, C.C. (eds.) Machine Learning and Statistics, The Interface, pp. 107–131. Wiley, NY (1997)

    Google Scholar 

  10. Brzeziñska, I., Greco, S., Sowiñski, R.: Mining Pareto-optimal rules with respect to support and confirmation or support and anti-support. Engineering Applications of Artificial Intelligence 20, 587–600 (2007)

    Article  Google Scholar 

  11. Duch, W., Adamczak, K., Grbczewski, K.: Methodology of extraction, optimization and application of crisp and fuzzy logical rules. IEEE Transaction on Neural Networks 12, 277–306 (2001)

    Article  Google Scholar 

  12. Furnkranz, J., Widmer, G.: Incremental Reduced Error Pruning. In: Proceedings of the Eleventh International Conference of Machine Learning, New Brunswick, NJ, USA, pp. 70–77 (1994)

    Google Scholar 

  13. Greco, S., Matarazzo, B., Sowiñski, R.: The use of rough sets and fuzzy sets in MCDM. In: Gal, T., Hanne, T., Stewart, T. (eds.) Advances in Multiple Criteria Decision Making, pp. 1–59. Kluwer Academic Publishers, Dordrecht (1999)

    Google Scholar 

  14. Greco, S., Materazzo, B., Sowiñski, R.: Rough sets theory for multicriteria decision analysis. European Journal of Operational Research 129, 1–47 (2001)

    Article  MATH  MathSciNet  Google Scholar 

  15. Greco, S., Pawlak, Z., Sowiñski, R.: Can Bayesian confirmation measures be use-ful for rough set decision rules? Engineering Applications of Artificial Intelligence 17, 345–361 (2004)

    Article  Google Scholar 

  16. Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley Publishing Company Inc., Boston (1989)

    MATH  Google Scholar 

  17. Góra, G., Wojna, A.: RIONA: A new classification system combining rule induction and instance-based learning. Fundamenta Informaticae 51(4), 369–390 (2002)

    MATH  MathSciNet  Google Scholar 

  18. Grzymaa-Busse, J.W.: LERS - a system for learning from examples based on rough sets. In: Sowiñski, R. (ed.) Intelligent Decision Support. Handbook of applications and advances of the rough set theory, pp. 3–18. Kluwer Academic Publishers, Dordrecht (1992)

    Google Scholar 

  19. Grzymaa-Busse, J.W., Ziarko, W.: Data mining based on rough sets. In: Wang, J. (ed.) Data Mining Opportunities and Challenges, pp. 142–173. IGI Publishing, Hershey (2003)

    Google Scholar 

  20. Guillet, F., Hamilton, H.J. (eds.): Quality Measures in Data Mining. Computational Intelligence Series. Springer, Heidelberg (2007)

    MATH  Google Scholar 

  21. Kanonenko, I., Bratko, I.: Information-based evaluation criterion for classifier‘s performance. Machine Learning 6, 67–80 (1991)

    Google Scholar 

  22. Kaufman, K.A., Michalski, R.S.: Learning in Inconsistent World, Rule Selection in STAR/AQ18. Machine Learning and Inference Laboratory Report P99-2 (February 1999)

    Google Scholar 

  23. Kubat, M., Bratko, I., Michalski, R.S.: Machine Learning and Data Mining: Methods and Applications. Wiley, NY (1998)

    Google Scholar 

  24. Latkowski, R., Mikoajczyk, M.: Data Decomposition and Decision Rule Joining for Classification of Data with Missing Values. In: Peters, J.F., Skowron, A., Grzymała-Busse, J.W., Kostek, B.z., Świniarski, R.W., Szczuka, M.S. (eds.) Transactions on Rough Sets I. LNCS, vol. 3100, pp. 299–320. Springer, Heidelberg (2004)

    Google Scholar 

  25. Michalski, R.S., Carbonell, J.G., Mitchel, T.M.: Machine Learning, vol. I. Morgan-Kaufman, Los Altos (1983)

    Google Scholar 

  26. Mikoajczyk, M.: Reducing number of decision rules by joining. In: Alpigini, J.J., Peters, J.F., Skowron, A., Zhong, N. (eds.) RSCTC 2002. LNCS (LNAI), vol. 2475, pp. 425–432. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  27. Nguyen, H.S., Nguyen, S.H.: Some Efficient Algorithms for Rough Set Methods. In: Proceedings of the Sixth International Conference, Information Processing and Management of Uncertainty in Knowledge-Based Systems, Granada, Spain, pp. 1451–1456 (1996)

    Google Scholar 

  28. Nguyen, H.S., Nguyen, T.T., Skowron, A., Synak, P.: Knowledge discovery by rough set methods. In: Callaos, N.C. (ed.) Proc. of the International Conference on Information Systems Analysis and Synthesis, ISAS 1996, Orlando, USA, July 22-26, pp. 26–33 (1996)

    Google Scholar 

  29. Nguyen, H.S., Skowron, A.: Searching for relational patterns in data. In: Komorowski, J., Żytkow, J.M. (eds.) PKDD 1997. LNCS, vol. 1263, pp. 265–276. Springer, Heidelberg (1997)

    Google Scholar 

  30. Nguyen, H.S., Skowron, A., Synak, P.: Discovery of data patterns with applications to decomposition and classfification problems. In: Polkowski, L., Skowron, A. (eds.) Rough Sets in Knowledge Discovery 2: Applications, Case Studies and Software Systems, pp. 55–97. Physica, Heidelberg (1998)

    Google Scholar 

  31. Nguyen, H.S.: Data regularity analysis and applications in data mining. Doctoral Thesis, Warsaw University. In: Polkowski, L., Tsumoto, S., Lin, T.Y. (eds.) Rough set methods and applications: New developments in knowledge discovery in information systems, pp. 289–378. Physica-Verlag/Springer, Heidelberg (2000), http://logic.mimuw.edu.pl/

    Google Scholar 

  32. Ohrn, A., Komorowski, J., Skowron, A., Synak, P.: The design and implementation of a knowledge discovery toolkit based on rough sets: The ROSETTA system. In: Polkowski, L., Skowron, A. (eds.) Rough Sets in Knowledge Discovery 1: Methodology and Applications, pp. 376–399. Physica, Heidelberg (1998)

    Google Scholar 

  33. Pawlak, Z.: Rough Sets. Theoretical aspects of reasoning about data. Kluwer Academic Publishers, Dordrecht (1991)

    MATH  Google Scholar 

  34. Pednault, E.: Minimal-Length Encoding and Inductive Inference. In: Piatetsky-Shapiro, G., Frawley, W.J. (eds.) Knowledge Discovery in Databases, pp. 71–92. MIT Press, Cambridge (1991)

    Google Scholar 

  35. Pindur, R., Susmaga, R., Stefanowski, J.: Hyperplane aggregation of dominance decision rules. Fundamenta Informaticae 61, 117–137 (2004)

    MATH  MathSciNet  Google Scholar 

  36. Podraza, R., Walkiewicz, M., Dominik, A.: Credibility coefficients in ARES Rough Sets Exploration Systems. In: Ślęzak, D., Yao, J., Peters, J.F., Ziarko, W.P., Hu, X. (eds.) RSFDGrC 2005. LNCS (LNAI), vol. 3642, pp. 29–38. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  37. Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan-Kaufman, San Mateo (1993)

    Google Scholar 

  38. Prêdki, B., Sowiñski, R., Stefanowski, J., Susmaga, R.: ROSE – Software implementation of the rough set theory. In: Polkowski, L., Skowron, A. (eds.) RSCTC 1998. LNCS (LNAI), vol. 1424, p. 605. Springer, Heidelberg (1998)

    Chapter  Google Scholar 

  39. Sikora, M., Proksa, P.: Algorithms for generation and filtration of approximate decision rules, using rule-related quality measures. In: Proceedings of International Workshop on Rough Set Theory and Granular Computing (RSTGC 2001), Matsue, Shimane, Japan, pp. 93–98 (2001)

    Google Scholar 

  40. Sikora, M.: Rules evaluation and generalization for decision classes descriptions improvement. Doctoral Thesis, Silesian University of Technology, Gliwice, Poland (2001) (in Polish)

    Google Scholar 

  41. Sikora, M., Proksa, P.: Induction of decision and association rules for knowledge discovery in industrial databases. In: International Conference on Data Mining, Alternative Techniques for Data Mining Workshop, Brighton, UK (2004)

    Google Scholar 

  42. Sikora, M.: Approximate decision rules induction algorithm using rough sets and rule-related quality measures. Theoretical and Applied Informatics 4, 3–16 (2004)

    MathSciNet  Google Scholar 

  43. Sikora, M.: An algorithm for generalization of decision rules by joining. Foundation on Computing and Decision Sciences 30, 227–239 (2005)

    Google Scholar 

  44. Sikora, M.: System for geophysical station work supporting - exploitation and development. In: Proceedings of the 13th International Conference on Natural Hazards in Mining, Central Mining Institute, Katowice, Poland, pp. 311–319 (2006) (in Polish)

    Google Scholar 

  45. Sikora, M.: Rule quality measures in creation and reduction of data role models. In: Greco, S., Hata, Y., Hirano, S., Inuiguchi, M., Miyamoto, S., Nguyen, H.S., Słowiński, R. (eds.) RSCTC 2006. LNCS (LNAI), vol. 4259, pp. 716–725. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  46. Sikora, M.: Adaptative application of quality measures in rules induction algorithms. In: Kozielski, S. (ed.) Databases, new technologies, vol. I. Transport and Communication Publishers (Wydawnictwa Komunikacji i Łączności), Warsaw (2007) (in Polish)

    Google Scholar 

  47. Skowron, A., Rauszer, C.: The Discernibility Matrices and Functions in Informa-tion systems. In: Sowiñski, R. (ed.) Intelligent Decision Support. Handbook of applications and advances of the rough set theory, pp. 331–362. Kluwer Academic Publishers, Dordrecht (1992)

    Google Scholar 

  48. Skowron, A., Stepaniuk, J.: Tolerance approximation spaces. In: Peters, J.F., Skowron, A. (eds.) Transactions on Rough Sets V. LNCS, vol. 4100, pp. 224–239. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  49. Skowron, A., Wang, H., Wojna, A., Bazan, J.: Multimodal Classification: Case Studies. Fundamenta Informaticae 27, 245–253 (1996)

    MATH  MathSciNet  Google Scholar 

  50. Sowiñski, R., Greco, S., Matarazzo, B.: Mining decision-rule preference model from rough approximation of preference relation. In: Proceedings of the 26th IEEE Annual Int. Conf. on Computer Software and Applications, Oxford, UK, pp. 1129–1134 (2002)

    Google Scholar 

  51. Stefanowski, J.: Rough set based rule induction techniques for classification problems. In: Proceedings of the 6th European Congress of Intelligent Techniques and Soft Computing, Aachen, Germany, pp. 107–119 (1998)

    Google Scholar 

  52. Stefanowski, J.: Algorithms of rule induction for knowledge discovery. Poznañ University of Technology, Thesis series 361, Poznañ, Poland (2001) (in Polish)

    Google Scholar 

  53. Smyth, P., Gooodman, R.M.: Rule induction using information theory. In: Piatetsky-Shapiro, G., Frawley, W.J. (eds.) Knowledge Discovery in Databases, pp. 159–176. MIT Press, Cambridge (1991)

    Google Scholar 

  54. Stepaniuk, J.: Knowledge Discovery by Application of Rough Set Models. Institute of Computer Sciences Polish Academy of Sciences, Reports 887, Warsaw, Poland (1999)

    Google Scholar 

  55. Stepaniuk, J., Krêtowski, M.: Decision System Based on Tolerance Rough Sets. In: Proceedings of the 4th International Workshop on Intelligent Information Systems, Augustów, Poland, pp. 62–73 (1995)

    Google Scholar 

  56. Ślęzak, D., Wróblewski, J.: Classification Algorithms Based on Linear Combination of Features. In: Żytkow, J.M., Rauch, J. (eds.) PKDD 1999. LNCS (LNAI), vol. 1704, pp. 548–553. Springer, Heidelberg (1999)

    Google Scholar 

  57. Wang, H., Duentsch, I., Gediga, G., Skowron, A.: Hyperrelations in version space. International Journal of Approximate Reasoning 36(3), 223–241 (2004)

    Article  MATH  MathSciNet  Google Scholar 

  58. Wojna, A.: Analogy based reasoning in classifier construction. In: Peters, J.F., Skowron, A. (eds.) Transactions on Rough Sets IV. LNCS, vol. 3700, pp. 277–374. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  59. Ziarko, W.: Variable precision rough sets model. Journal of Computer and System Sciences 46, 39–59 (1993)

    Article  MATH  MathSciNet  Google Scholar 

  60. Zhong, N., Skowron, A.: A rough set-based knowledge discovery process. International Journal of Applied Mathematics and Computer Sciences 11, 603–619 (2001)

    MATH  MathSciNet  Google Scholar 

  61. Yao, Y.Y., Zhong, N.: An Analysis of Quantitative Measures Associated with Rules. In: Zhong, N., Zhou, L. (eds.) PAKDD 1999. LNCS (LNAI), vol. 1574, pp. 479–488. Springer, Heidelberg (1999)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Sikora, M. (2010). Decision Rule-Based Data Models Using TRS and NetTRS – Methods and Algorithms. In: Peters, J.F., Skowron, A. (eds) Transactions on Rough Sets XI. Lecture Notes in Computer Science, vol 5946. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11479-3_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-11479-3_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-11478-6

  • Online ISBN: 978-3-642-11479-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics