Author:
Yazirli Yonca,Kan-Kilinç Betül
Abstract
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.
Publisher
Granthaalayah Publications and Printers
Reference19 articles.
1. 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.
2. Pawlak, Z. Rough sets, International Journal of Computer and Information Science, vol.11, no.5,1982, 341-356.
3. Johnson, D. Approximation algorithms for combinatorial problems, Journal of Computer and System Sciences, 9, 1974, 256-278.
4. Wroblewski, J. Finding minimal reducts using genetic algorithms, Second Annual Join Conference on Information Sciences, 1995, 186-189.
5. 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.