Multiclassification Prediction of Clay Sensitivity Using Extreme Gradient Boosting Based on Imbalanced Dataset

Predicting clay sensitivity is important to geotechnical engineering design related to clay. Classification charts and field tests have been used to predict clay sensitivity. However, the imbalanced distribution of clay sensitivity is often neglected, and the predictive performance could be more accurate. The purpose of this study was to investigate the performance that extreme gradient boosting (XGboost) method had in predicting multiclass of clay sensitivity, and the ability that synthetic minority over-sampling technique (SMOTE) had in addressing imbalanced categories of clay sensitivity. Six clay parameters were used as the input parameters of XGBoost, and SMOTE was used to deal with imbalanced classes. Then, the dataset was divided using the cross-validation (CV) method. Finally, XGBoost, artificial neural network (ANN), and Naive Bayes (NB) were used to classify clay sensitivity. The F1 score, receiver operating characteristic (ROC), and area under the ROC curve (AUC) were considered as the performance indicators. The results revealed that XGBoost showed the best performance in the multiclassification prediction of clay sensitivity. The F1 score and mean AUC of XGBoost were 0.72 and 0.89, respectively. SMOTE was useful in addressing imbalanced issues, and XGBoost was an effective and reliable method of classifying clay sensitivity.

Automated summary

This study investigates the performance of extreme gradient boosting (XGBoost) in predicting multiclass clay sensitivity and the ability of synthetic minority over-sampling technique (SMOTE) in addressing imbalanced categories. The results show that XGBoost performs the best in the prediction of clay sensitivity, while SMOTE is useful in addressing imbalanced issues.