Analysis of the Influences of Sampling Bias and Class Imbalance on Performances of Probabilistic Liquefaction Models

Abstract

AbstractSampling bias and class imbalance are important parts of model uncertainty that have a significant impact on the predictive probability of classification models. This study analyzed the influences of sampling bias and class imbalance on the performance of four common methods used in 10 models for seismic liquefaction—Bayesian network (BN), artificial neural network (ANN), logistic regression (LR), and support vector machine (SVM)—using controlled experiments based on penetration test (SPT) data from 350 standard case histories. The data are divided into two data sets with class distributions of 150:150 and 200:100, which are separately stratified and sampled to obtain 11 different cases of distributions (10:90, 20:80, 25:75, 33:67, 40:60, 50:50, 60:40, 67:33, 75:25, 80:20, and 90:10) to quantify the predictive performance of the four models using statistical model validation metrics, such as overall accuracy, area under the receiver operating characteristic curve, precision, recall, and F-score. T...