Abstract：Machine learning rockburst predictions seek to solve the data imbalance problem and improve the prediction accuracy and model generalization. This work optimized a rockburst prediction algorithm using a dataset containing 246 rockburst cases and 6 rockburst indicators, B1, MTS, SCF, UCS, UTS and Wet. Nine rockburst prediction models were developed based on machine learning algorithms to study the effects of 5 oversampling methods and 5 objective weighting methods on the model predictions. The results show that data oversampling improves the model accuracy by 11.8% to 52.3% and increases the macro average F1 by 13.0% to 50.0%. Random oversampling gives the best improvements to resolve the data imbalance problem. After randomly oversampling the data set, objective date weighting only increases the accuracy and the macro average F1 of the extreme gradient boosting algorithm (XGBoost) model by 1.1% and 1.2%, the random forest (RF) model by 2.1% and 2.3%, the decision tree (DT) model by 10.7% and 11.8%, and the extremely randomized trees (ET) model by 12.9% and 12.8%. The multi-layer perception (MLP) algorithm model based on random oversampling is the best rockburst prediction model with an accuracy of 0.917 and a macro average F1 of 0.920.
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