COMPARISON OF ALGORITHMS BASED ON ROUGH SET THEORY FOR A 3-CLASS CLASSIFICATION

Authors

  • 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

DOI:

https://doi.org/10.29121/granthaalayah.v7.i8.2019.689

Keywords:

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.

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Published

2019-08-31

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. https://doi.org/10.29121/granthaalayah.v7.i8.2019.689