Developing intelligent classification models for rock burst prediction after recognizing significant predictor variables, Section 1: Literature review and data preprocessing procedure

Rock bursts constitute serious hazards in underground mining and excavating. Up to now, numerous researches in the form of empirical, experimental, analytical, intelligent and numerical methods with their own specific scope, characteristics, strengths and weaknesses, have been conducted for rock burst prediction. The weaknesses and limitations of the mentioned prediction methods, especially the intelligent studies, indicate the need for continuing the researches in this field. In this research, a rock burst database, consisting of 188 datasets, was considered. Each dataset corresponds to a series of predictor variables and one of defined classes for the dependent variable rock burst intensity. To design classification models, describing important characteristics of datasets and predicting future trends, a data preprocessing procedure was conducted. The procedure consisted of a statistical analysis strategy, a metaheuristic technique for feature (variable) subset selection and some feature extraction techniques. The statistical analysis led to conclude that by considering the available datasets, some predictor variables have statistically insignificant contributions for rock burst prediction. By contrast, the other predictor variables have considerable ordinal contributions. These statistical inferences were completely in accordance with the results of the feature subset selection technique. Besides, the application of this technique revealed specific combinations of significant predictor variables having the highest priorities for modelling the dependent variable. The application of feature extraction techniques to construct derived components from initial datasets did not lead to representative results. Therefore, a high rank combination of significant predictor variables can be adopted to design and develop new classification models based on the considered datasets.