Performance of the Hybrid Approach based on Rough Set Theory
| Autor: | Betul Kan Kilinc, Yonca Yazirli |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Statistics and Probability
business.industry 0211 other engineering and technologies 02 engineering and technology Management Science and Operations Research Hybrid approach 020303 mechanical engineering & transports 0203 mechanical engineering Modeling and Simulation 021105 building & construction Artificial intelligence Rough set Statistics Probability and Uncertainty business Mathematics |
| Zdroj: | Pakistan Journal of Statistics and Operation Research. :217-224 |
| ISSN: | 2220-5810 1816-2711 |
| DOI: | 10.18187/pjsor.v16i2.3069 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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