• Yonca Yazirli Institute of Graduate Programs, Department of Statistics, Eskisehir Technical University, Eskisehir, Turkey
  • Betül Kan-Kilinç Department of Statistics, Faculty of Science, Eskisehir Technical University, Eskisehir, Turkey



Attribute Reduction, Rough Set Theory, Classification, Real Estate

Abstract [English]

There are various data mining techniques to handle with huge amount of data sets. Rough set based classification provides an opportunity in the efficiency of algorithms when dealing with larger datasets. The selection of eligible attributes by using an efficient rule set offers decision makers save time and cost. This paper presents the comparison of the performance of the rough set based algorithms: Johnson’ s, Genetic Algorithm and Dynamic reducts. The performance of algorithms is measured based on accuracy, AUC and standard error for a 3-class classification problem on training on test data sets. Based on the test data, the results showed that genetic algorithm overperformed the others.


Download data is not yet available.


Kusiak A., Data Mining in Design of Products and Production Systems, Proceedings of INCOM’2006: 12th IFAC/IFIP/IFORS/IEEE Symposium on Control Problems in Manufacturing, May 2006, Saint-Etienne, France, 1, 2006, 49-53. DOI:

Pawlak, Z. Rough sets, International Journal of Computer and Information Science, vol.11, no.5,1982, 341-356. DOI:

Johnson, D. Approximation algorithms for combinatorial problems, Journal of Computer and System Sciences, 9, 1974, 256-278. DOI:

Wroblewski, J. Finding minimal reducts using genetic algorithms, Second Annual Join Conference on Information Sciences, 1995, 186-189.

Al-Radaideh, Q. A., Sulaiman, M. N., Selamat, M. H., Ibrahim, H. Approximate reduct computation by rough sets based attribute weighting, 2005 IEEE International Conference on Granular Computing, Beijing, China, 2005, 25-27 July. DOI:

Swiniarski, R.W., Skowron, A. Rough set methods in feature selection and recognition, Pattern Recognition Letters, vol. 24, no. 6, 2003, 833–849. DOI:

Zeng, A., Pan, D., Zheng, Q. L., Peng, H. Knowledge acquisition based on rough set theory and principal component analysis, IEEE Intelligent Systems, vol. 21, issue 2, 2006, 78-85. DOI:

Srivastava, D. K., Patnaik, K. S., Bhambhu, L. Data classification: A Rough-SVM approach, Contemporary Engineering Sciences, Vol. 3, no. 2, 2010, 77 – 86.

Yamany, W., Emary, E., Hassanieh, A.E., Schaefer, G. Zhu, S. Y. An Innovative Approach for Attribute Reduction using Rough Sets and Flower Pollination Optimisation, Procedia Compuer Science, 96, 2016, 403-409. DOI:

Pawlak, Z. Rough Sets Theoretical Aspect of Reasoning about Data. Boston, Mass, Kluwer Academic, 1991. DOI:

Pawlak, Z., Grzymala-Busse, J., Slowinski, R. and Ziarko, W. Rough sets, Communications of the ACM, vol. 38, no. 11, 1995, 89–95. DOI:

Skowron, A., Rauszer, C. The discernibility matrices and functions in information systems, in Slowifiski R.(ed.), Intelligent Decision Support. Handbook of Applications and Advances of the Rough Sets Theory. Kluwer, Dortrecht.1992, 331-362. DOI:

Zhao, Y., Yao, Y. and Luo, F. Data analysis based on discernibility and indiscernibility, Information Sciences, 177(22), 2007, 4959–4976. DOI:

Vinterbo, S., Øhrn, A. Minimal approximate hitting sets and rule templates, International Journal of Approximate Reasoning, 25, 2000, 123-143. DOI:

Godinez, F., Hutter, D., Monroy, R., Attribute Reduction for Effective Instrusion Detection, Advances in Web Intelligence, Second International Atlantic Web Intelligence Conference, AWIC, Cancun, Mexico, 2004, May 16-19.

Bazan J.G., Skowron A., Synak P. Dynamic reducts as a tool for extracting laws from decisions tables. In: Raś Z.W., Zemankova M. (eds) Methodologies for Intelligent Systems. ISMIS 1994. Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence), vol 869. Springer, 1994, Berlin, Heidelberg. DOI:

Bazan, J. G. (1998) “A comparison of dynamic and non-dynamic rough set methods for extracting laws from decision tables”. Rough Sets in Knowledge Discovery 1: Methodology and Applications, volume 18 of Studies in Fuzziness and Soft Computing, Heidelberg, Germany Physica-Verlag, 1998, Chapter 17, pages 321–365.

EVDS Data Central. URL:, Accessed Date:14.02.2019.

Øhrn, A. ROSETTA Technical Reference Manual. Trondheim, Norway, 2001, Department of Computer and Information Science, Norwegian University of Science and Technology.




How to Cite

Yazirli, Y., & Kan-Kilinç, B. (2019). COMPARISON OF ALGORITHMS BASED ON ROUGH SET THEORY FOR A 3-CLASS CLASSIFICATION. International Journal of Research -GRANTHAALAYAH, 7(8), 394–401.