Integrated framework for feature extraction and weighting in coal and gas outburst classification

Abstract

The prediction of coal and gas outburst is very necessary for the prevention of gas disaster, so an outburst prediction model coupled with feature extraction and feature weighting using optimized classifier is proposed. First, Pearson correlation coefficient(PCC) and symmetric uncertainty(SU) are employed to measure the effective information in outburst sample data. Second, Kernel principal component analysis(KPCA) and linear discriminant analysis(LDA) methods are used to extract the exiting discriminate information, and the extracted linear and nonlinear feature information can effectively reflect significant information of outburst influencing factors. Third, the combination of gradient boost decision tree(GBDT) and grey relation analysis(GRA) is used to weight and fuse the extracted linear and nonlinear feature components, then form a new feature set as important discriminant information. Forth, the weighted and fused features of the coal and gas outburst influencing factors are used as the input of support vector machine(SVM) classifier with optimized parameters, it can classify outburst states, and the achieved classification accuracy can obtain 95%. Finally, the proposed model and the existing outburst classification models in literatures are used to predict outburst, then the experiment results verify the effectiveness of the proposed model and conclude that the performance of the proposed predication model are significant than present outburst prediction models.