## 1 Introduction
### Current Use of Machine Learning Algorithms for Rock Burst Prediction  
The prediction of rock bursts using machine learning algorithms provides an alternative approach adopted by many researchers, which is based on learning by experience. Machine learning algorithm models are trained without the need to determine the cause of rock bursts. Supervised machine learning algorithms have mainly been used to classify stress rock bursts into appropriate intensity levels. This is understandable because rock bursts of this type most often occur during the underground mining of various mineral resources and tunneling. The parameters taken into account mainly concern the stress level and strength parameters of rocks in which the opening is drilled such as the maximum tangential stress, the uniaxial tensile strength, the uniaxial compressive strength, and the elastic energy index. For example, [PERSON] et al. [20] trained an artificial neural network model based on 20 rock bursts that occurred during tunneling. [PERSON] et al. [21] applied artificial neural networks for predicting rock bursts and their intensity, and the total database included 18 cases. [PERSON] and [PERSON] [22] proposed the use of artificial neural networks as weak classifiers in the AdaBoost algorithm to categorize and predict rock bursts and considered 36 rock burst cases. [PERSON] et al. [23] applied the k-nearest neighbor algorithm for rock burst prediction at a great depth in South Africa. [PERSON] et al. [24] proposed a rock burst intensity prediction model based on Gaussian process (GP). The model was trained on thirteen cases and tested on five cases of rock bursts during geoengineering works in different countries. [PERSON] et al. [25] used a Bayes discriminant analysis method to predict the possibility and classification of rock bursts, and the database included 14 cases. [PERSON] et al. [26] used the support vector machine (SVM) algorithm for rock burst prediction. Their database contained 132 rock bursts in openings drilled in various rocks (e.g., granite, sandstone, diorite, rhyolite). [PERSON] et al. [27] proposed the particle swarm optimization (PSO) algorithm and the general regression neural network (GRNN). The database contained 26 cases. [PERSON] et al. [28] combined neural networks with fuzzy logic for predicting the intensity of rock bursts, and the dataset consisted of 174 rock bursts. [PERSON] et al. [29] compared the ability of the following machine learning algorithms to predict rock burst categories: linear discriminant analysis, quadratic discriminant analysis, partial least-squares discriminant analysis, naive Bayes, k-nearest neighbor, multilayer perceptron neural network, classification tree, support vector machine, random forest, and gradient-boosting machine, and their dataset included 246 rock bursts. [PERSON] et al. [30] applied Bayesian networks for rock burst prediction, and their rock burst database contained 135 cases. [PERSON] and [PERSON] [31] applied genetic algorithm-based emotional neural network (GA-ENN), gene expression programming (GEP), and decision tree-based C4.5 algorithm to predict the occurrence of rock burst in a binary condition and their dataset included 134 rock bursts. [PERSON] and [PERSON] [32] used the logistic regression classifier for the long-term prediction of rock burst. The database contained 135 cases (i.e., 83 rock bursts and 52 non-rock burst events). [PERSON] et al. [33] applied a decision tree model to predict rock burst potential in kimberlite at an underground diamond mine. The database contained 132 rock bursts including 108 complete samples and 24 incomplete samples. [PERSON] et al. [34] applied the entropy weight gray relational backpropagation (BP) neural network to predict the intensity grade of rock burst and the database used to train the model contained 20 rock bursts. [PERSON] et al. [35] applied a decision tree algorithm to predict the occurrence and intensity of rock burst and the database contained 174 rock bursts. [PERSON] et al.