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One of the essential problems in data mining is the removal of negligible variables from the data set. This paper proposes a hybrid approach that uses rough set theory based algorithms to reduct the attribute selected from the data set and utilize reducts to raise the classification success of three learning methods; multinomial logistic regression, support vector machines and random forest using 5-fold cross validation. The performance of the hybrid approach is measured by related statistics. The results show that the hybrid approach is effective as its improved accuracy by 6-12% for the three learning methods.


Rough set Reduction Performance Accuracy

Article Details

Author Biography

Betul Kan Kilinc, Eskisehir Technical University Department of Statistics 26470 Eskisehir Turkey

Department of Statistics
How to Cite
Kan Kilinc, B., & YAZIRLI, Y. (2020). Performance of the Hybrid Approach based on Rough Set Theory. Pakistan Journal of Statistics and Operation Research, 16(2), 217-224.


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