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

Abstract

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.

Artificial Intelligence (AI) Applied in Civil Engineering

“…The proposed model can provide a reliable reference for the thickness prediction of an excavation-damaged zone and is helpful in the risk management of roadway stability. Ma et al [29] investigate the performance of the extreme gradient boosting (XGboost) method in predicting multiclass of clay sensitivity, and the ability of the synthetic minority over-sampling technique (SMOTE) in addressing imbalanced categories of clay sensitivity. The results reveal that XGBoost shows the best performance in the multiclassification prediction of clay sensitivity.…”

“…Three MDPI journals participated by cross-listing the research topic. Most of the articles (29) were published in the "Applied Sciences" journal, while 3 of them were published in "Mathematics" and another 3 in "Symmetry".…”

Application of GA-WELM Model Based on Stratified Cross-Validation in Intrusion Detection

“…However, WELM increases the detection rate of small sample data at the expense of large sample data. Ma et al [30] used a crossvalidation method to divide the dataset, then used extreme gradient boosting (XGboost) to classify it. The F1 value and average AUC of this method are 0.72 and 0.89, respectively.…”