Application of coupled FICA-FCM feature extraction and QPSO-SVM classifier model to predict coal and gas outbursts

Abstract

The coal and gas outbursts samples data are affected by all kinds of influencing factors, the accuracy of classification on these sample data is not high, the classification of some samples always have errors, which may be inaccurate annotation. In order to reduce the impact of noise data caused by the labeling errors on classification, this paper proposes a combination of classifier and clustering analysis model, which is used to improve the prediction accuracy of coal and gas outbursts. First, the high-order statistical characteristics of fast independent component analysis(FICA) method are used to extract the features of coal and gas outbursts sample data to obtain the independent nonlinear main metadata; Then, the fuzzy clustering mean(FCM) algorithm is used to cluster the coal and gas outbursts samples. Finally, based on improved clustering analysis results, the support vector machine(SVM) classifier based on quantum particle swarm optimization(QPSO) algorithm is used to compare the classification results with the existing labeled results, and the classification results are corrected to improve the classification accuracy. The results show that the performance of coupling model is significantly better than other exiting classifiers for coal and gas outbursts prediction.