Abstract
The NetTRS system is a web service that makes induction, evaluation and postprocessing of decision rules possible. The TRS library is the kernel of the system. It allows to induce rules by means of the tolerance rough sets model. The NetTRS makes user interface of the TRS library available in the Internet. The main emphasis of the NetTRS system is placed on induction and postprocessing of decision rules. This article shows the architecture and the functionality of the system. This paper describes also the parameterization of algorithms that are implemented in the TRS library.
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References
An, A., Cercone, N.: Rule quality measures for rule induction systems– description and evaluation. Computational Intelligence 17, 409–424 (2001)
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)
Bazan, J., Latkowski, R., Szczuka, M.: DIXER– distributed executor for rough set exploration system. In: Ślęzak, D., Yao, J., Peters, J.F., Ziarko, W.P., Hu, X. (eds.) RSFDGrC 2005. LNCS (LNAI), vol. 3642, pp. 39–47. Springer, Heidelberg (2005)
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)
Grzymała-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)
Witten, I.H., Frank, E.: Data Mining: Practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)
Kubat, M., Bratko, I., Michalski, R.S.: Machine Learning and Data Mining: Methods and Applications. Wiley, NY (1998)
Michalski, R.S., Carbonell, J.G., Mitchel, T.M.: Machine Learning, vol. I. Morgan-Kaufman, Los Altos (1983)
Mitchell, M.: An Introduction to Genetic Algorithms. MIT Press, USA (1996)
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)
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-Verlag, Heidelberg (1998)
Pawlak, Z.: Rough Sets. Theoretical aspects of reasoning about data. Kluwer Academic Publishers, Dordrecht (1991)
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, vol. 3642, pp. 29–38. Springer, Heidelberg (2005)
Prȩdki, B., Słowiński, R., Stefanowski, J., Susmaga, R.: ROSE – Software implementation of the rough set theory. In: Polkowski, L., Skowron, A. (eds.) RSCTC 1998. LNCS, vol. 1424, pp. 605–608. Springer, Heidelberg (1998)
Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan-Kaufman, San Mateo (1993)
Quinlan, J.R.: Combining instance-based learning and model-based learning. In: Proceedings of the Tenth International Conference on Machine Learning (ML 1993), Massachusetts, Amherst, MA, USA, pp. 236–243 (1993)
Sikora, M.: Decision rules-based data models using NetTRS – methods and algorithms, NetTrs Website, Silesian Univesity of Technology, Gliwice, Poland (2006), http://nettrs.polsl.pl/nettrs/Examples/NeTTRSalg.pdf
Sikora, M., Michalak M.: Decision rules-based data models using NetTRS – users guide, NetTrs Website, Silesian Univesity of Technology, Gliwice, Poland (2007), http://nettrs.polsl.pl/nettrs/Examples/Usersguide.pdf
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 Ła̧czności), Warsaw, Poland (2007) (in Polish)
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)
Sikora, M.: An algorithm for generalization of decision rules by joining. Foundation on Computing and Decision Sciences 30, 227–239 (2005)
Sikora, M.: Approximate decision rules induction algorithm using rough sets and rule-related quality measures. Theoretical and Applied Informatics 4, 3–16 (2004)
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)
Skowron, A., Rauszer, C.: The Discernibility Matrices and Functions in Informa-tion systems. In: Słowiński, R. (ed.) Intelligent Decision Support. Handbook of applications and advances of the rough set theory, pp. 331–362. Kluwer Academic Publishers, Dordrecht (1992)
Stefanowski, J.: Rough set based rule induction techniques for classification problems. In: Proceedings of the 6th European Congress of Intelligent Techniques and Soft Computing, Achen, Germany, pp. 107–119 (1998)
Stepaniuk, J.: Optimizations of Rough Set Model. Fundamenta Informaticae 25, 1–19 (1998)
Stepaniuk, J.: Knowledge Discovery by Application of Rough Set Models. Institute of Computer Sciences Polish Academy of Sciences, Reports 887, Warsaw, Poland (1999)
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Michalak, M., Sikora, M. (2008). Decision Rule Based Data Models Using NetTRS System Overview. In: Peters, J.F., Skowron, A., Rybiński, H. (eds) Transactions on Rough Sets IX. Lecture Notes in Computer Science, vol 5390. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89876-4_9
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DOI: https://doi.org/10.1007/978-3-540-89876-4_9
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