TRS Library – tool for inducing and postpocessing of decision rules

Marek Sikora, Aleksandra Gruca, Robert Gruca


This paper presents TRS library, which implements decision rules induction algorithms based on tolerance rough sets model. Algorithms used for filtering and approximating rules proposed by their authors are significant part of the library. Until now the library was available as a binary file or using command line interpreter. Recently graphical user interface was created to simplify experiments and analysis.


decision rules; filtering rules; generalizing rules; classification

Full Text:



Bazan J. G., Szczuka M. S., Wróblewski J.: A New Version of Rough Set Exploration System. Lecture Notes in Artificial Intelligence 2475, Berlin, Heidelberg: Springer-Verlag, 2002, pp.397-404.

Bruha I.: Quality of Decision Rules: Definitions and Classification Schemes for Multiple Rules. Nakhaeizadeh G., Taylor C. C. (ed.) Machine Learning and Statistics, The Interface. John Wiley and Sons, 1997

Grzymała-Busse J.: LERS – a system for learning from examples based on rough sets. Słowiński R. (ed.): Intelligent Decision Support. Dordrecht. Kluwer 1992, pp.3-18.

Goldberg D. E.: Algorytmy genetyczne i ich zastosowania. Wydawnictwo Naukowo-Techniczne, Warszawa 1998.

Michalski R. S., Bratko I.: Machine Learning and Data Mining: Methods and Appli-cations. John Wiley and Sons, 1998.

Nguyen H. S., Nguyen S. H.: Some efficient algorithms for rough set methods. Proceedings of the Sixth International Conference, IPMU'96 2, July 1-5, Granada, Spain pp. 1451-1456.

Ohrn A.: Rosetta technical users manual. Knowledge Systems Group, Dept. of Computer and Information Science, NTNU, Norway, 2001. (

Pawlak Z.: Rough sets: Theoretical aspects of reasoning about data. Dordrecht: Kluwer, 1991

Proksa P., Sikora M.: Application of Genetic Algorithms to Create Rule Description of Decision Classes. V Krajowa Konferencja Algorytmy Ewolucyjne i Optymalizacja Globalna. Jastrzębia Góra, 30.05-1.06, 2001, pp. 178-196.

Sikora M.: Filtracja zbioru reguł decyzyjnych wykorzystująca funkcje oceny jakości reguł. Studia Informatica Vol. 46, No. 4, Gliwice 2001.

Sikora M., Proksa P.: Algorithms for generation and filtration of approximate decision rules, using rule-related quality measures. Bulletin of International Rough Set Society Vo. 5, No. 1/2 .Proceedings of RSTGC-2001, 2001, pp.

Sikora M., Proksa P.: Induction of decision and association rules for knowledge discovery in industrial databases. DM-IEEE, IEEE International Conference of Data Mining, Brighton, 01-04, November 2004.

Sikora M.: Approximate decision rules induction algorithm using rough sets and rule-related quality measures. Archiwum Informatyki Teoretycznej i Stosowanej (w druku).

Stefanowski J.: Rough set based rule induction techniques for classification problems. Proc. 6-th European Congress for Intelligent Techniques and Soft Computing, vol.1, Aachen, Sept. 7-10, 1998, pp.107-119.

Stepaniuk J.: Knowledge Discovery by Application of Rough Set Models. ICS PAS Reports No. 887, Warszawa, 1999.