基于非均衡样本集的煤矿突水预测模型

Coal Mine Water Inrush Prediction Model Based on Unbalanced Set of Samples

针对煤矿突水样本集呈非均衡分布的特点,提出基于集成学习分类的煤矿突水预测模型,重点研究基分类器的构建方法、性能衡量指标和权重分析,以及基于改进型Boosting的集成学习算法.实验结果表明,该算法以牺牲不突水样本的最小误判率为代价,实现突水样本100%的判别准确率,且计算量小,易于实现.

Taking the non-equilibrium distribution characteristics of the coal mine water burst sample set into account, this study presents a coal mine water inrush prediction model based on the integrated learning classification. It focuses on the construction method of base classifier, the performance index and the weight analysis of base classifier, and the integrated learning algorithm based on improved Boosting. The experimental results show that although the algorithm does not achieve the minimum error rate of non-waterlogging samples, a 100% discrimination rate for water burst samples is realized, and the calculation load is small and it is easy to realize.