%0 Journal Article
%T 基于集成学习的煤与瓦斯突出预测研究<br>Research on Prediction of Coal and Gas Outburst Based on Integrated Learning
%A 张杰
%A 邓森
%J Mine Engineering
%P 142-148
%@ 2329-731X
%D 2023
%I Hans Publishing
%R 10.12677/ME.2023.112018
%X 为了提升煤与瓦斯突出事故预测的准确性和可行性，使用主成分分析法对影响煤与瓦斯突出的12个影响因素的原始数据进行降维处理，进而得到包含原始数据85%信息量的8个主成分，以此8个主成分作为输入通过AdaBoost并以单层决策树作为弱分类器进行学习，建立起主成分分析法与AdaBoost相结合的煤与瓦斯突出预测模型。并选取实例利用64组数据为训练样本，16组为预测样本，通过混淆矩阵判断证明模型的稳定性。结果表明：基于AdaBoost算法以单层决策树为弱分类器的预测模型预测精度达到100%，且总体水平稳定，可为安全生产提供理论依据。<br />
In order to improve the accuracy and feasibility of coal and gas outburst accident prediction, prin-cipal component analysis is used to reduce the dimensionality of the original data of 12 factors af-fecting coal and gas outburst, and then the information content containing 85% of the original data is obtained. The 8 principal components are used as input through Adaboost and the single- layer decision tree is used as a weak classifier to learn, and a coal and gas outburst prediction model combining principal component analysis and AdaBoost is established. And select examples to use 64 sets of data as training samples and 16 sets as prediction samples, and prove the stability of the model by judging the confusion matrix. The results show that the prediction accuracy of the prediction model based on the AdaBoost algorithm and the single-layer decision tree as the weak classifier reaches 100%, and the overall level is stable, which can provide a theoretical basis for safe production.
%K 集成学习，决策树，煤与瓦斯突出，预测<br>Ensemble Learning
%K Decision Tree
%K Coal and Gas Outburst
%K Prediction
%U http://www.hanspub.org/journal/PaperInformation.aspx?PaperID=63801