Main Article Content
Abstract
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.
Keywords
Rough set
Reduction
Performance
Accuracy
Article Details
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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. https://doi.org/10.18187/pjsor.v16i2.3069
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