Evaluation of Oversampling Methods (OVER, SMOTE, and ROSE) in Classifying Soil Liquefaction Dataset based on SVM, RF, and Naïve Bayes

Abstract

Class imbalanced datasets are prevalent in real-world applications, including engineering, medical domain, financial sector, and others. Machine learning (ML)-based prediction models have successfully demonstrated the applicability of various algorithms for the solution of different problems. However, their application for the soil liquefaction issue considering the class imbalance situation is limited. This paper presents the prediction results of random forest (RF), support vector machine (SVM), and naïve bayes (NB) algorithms with different training sample sizes for soil liquefaction. The effect of oversampling methods, namely simple oversampling (OVER), random oversampling examples (ROSE), and synthetic minority oversampling technique (SMOTE), on the prediction performance of classification algorithms is also investigated. Performance results are evaluated by means of some metrics, including Accuracy, Kappa, Precision, Recall, and F-measure. The results concluded the effectiveness of applying oversampling methods on imbalanced data before the modeling phase. All of the oversampling methods helped to enhance the overall performances of the classification models. It is also observed that the SMOTE exhibited slightly better performance than other considered oversampling methods. Furthermore, the SVM model outperformed compared to RF and NB models when all algorithms were trained by the SMOTE algorithm.