Bagged Ensemble of Gaussian Process Classifiers for Assessing Rockburst Damage Potential with an Imbalanced Dataset

Abstract

The evaluation of rockburst damage potential plays a significant role in managing rockburst risk and guaranteeing the safety of personnel. However, it is still a challenging problem because of its complex mechanisms and numerous influencing factors. In this study, a bagged ensemble of Gaussian process classifiers (GPCs) is proposed to assess rockburst damage potential with an imbalanced dataset. First, a rockburst dataset including seven indicators and four levels is collected. To address classification problems with an imbalanced dataset, a novel model that integrates the under-sampling technique, Gaussian process classifier (GPC) and bagging method is constructed. Afterwards, the comprehensive performance of the proposed model is evaluated using the values of accuracy, precision, recall, and F1. Finally, the methodology is applied to assess rockburst damage potential in the Perseverance nickel mine. Results show that the performance of the proposed bagged ensemble of GPCs is acceptable, and the integration of data preprocessing, under-sampling technique, GPC, and bagging method can improve the model performance. The proposed methodology can provide an effective reference for the risk management of rockburst.