SMOTE vs. KNNOR: An evaluation of oversampling techniques in machine learning

The increasing availability of big data has led to the development applications that make human life easier. In order process this correctly, it is necessary extract useful and valid information from large warehouses through a knowledge discovery in databases (KDD). Data mining an important part involves discovering developing models unknown patterns. quality used supervised machine learning algorithms plays significant role determining success predictions. One factor improves balanced dataset, where input values are distributed close each other. However, practice, many datasets unbalanced. To overcome problem, oversampling techniques generate synthetic as real possible. study, we compared performance two techniques, SMOTE KNNOR, on variety using different algorithms. Our results showed use KNNOR did not always improve accuracy model. fact, datasets, these resulted decrease accuracy. certain both were able increase indicate effectiveness varies depending specific dataset algorithm being used. Therefore, crucial assess methods case-by-case basis determine best approach for given algorithm.