辽宁工程技术大学学报（自然科学版）2017，Vol.36Issue(7)：679-683,5.DOI:10.11956/j.issn.1008-0562.2017.07.002
局部加权随机森林的冲击地压危险性等级预测
Prediction of rock burst grade based on locally weighted random forest
摘要
Abstract
In order to predict the risk grade of rock burst,thickness of coal seam,dip angle of coal seam,mining depth,roof lithology,tectonic conditions,mining method,coal pillar and mining craft were considered as the influence factors.Locally weighted learning (LWL) method was selected to build the prediction model,in which random forest was selected as the classifier,and Euclidean distance function was selected to compute the distance of the samples.17 groups of samples were chosen and 14 in which were trained to create the prediction model.Then it was compared with those created by Decision Tree and Naive Bayes using 10-fold cross-validation.It showed that the forecast accuracy had been greatly improved.Then the rest of the samples were predicted by the model,and the classification results were according with the actual.The resutel of study shows that locally weighted random forest can build much better modle with high generalization performanace.关键词
冲击地压/等级预测/局部加权学习/随机森林/十折交叉验证Key words
rock burst/grade prediction/local weighted learning/random forest/10-fold cross-validation分类
矿业与冶金引用本文复制引用
王彦彬,田洪斌,李昕璐..局部加权随机森林的冲击地压危险性等级预测[J].辽宁工程技术大学学报（自然科学版）,2017,36(7):679-683,5.基金项目
国家自然科学基金(71371091) （71371091）
