基于信息增益优化支持向量机模型的煤矿瓦斯爆炸风险预测

为了探索基于样本数据的煤矿瓦斯爆炸风险预测,依据本质安全理念构建了预测瓦斯爆炸风险的指标集,结合机器学习与特征优化算法提出了信息增益(information gain,IG)与支持向量机(support vector machine,SVM)的组合模型,通过对优化后的14种特征信息的分类学习,完成对风险未知样本的预测任...

Risk prediction of coal mine gas explosion based on ig-svm model

In order to explore the risk prediction of coal mine gas explosion based on the sample data, an index set for predicting gas explosion risk based on intrinsic safety concept is established, An immune support vector machine combined with machine learning and feature optimization algorithm is proposed, the prediction task of unknown risk samples was completed through the classification learning of 14 optimized feature information. 100 coal mining companies across the country were selected as the research objects, Different models are used to predict the risk of gas explosion, and are comprehensively analyzed and compared. The experimental results show that the accuracy of SVM model after IG optimization reaches 95.45%, Compared with the single SVM model, it is increased by 9.09%, and higher than other prediction models, which proves the superiority of the combined model in the field of gas explosion risk prediction.