摘要
针对围岩分类问题,提出了一种依据数据挖掘技术,采用二叉树支持向量机的智能围岩分类方法。该方法选择9项影响围岩分类的主要指标,利用SPSS Modeler数据挖掘工具构建SVM分类拟合模型,对围岩数据进行分类和预测,以实现快速智能化决策输出。研究结果表明:基于数据挖掘的支持向量机围岩分类方法可以很好地解决小样本、非线性、高维数的问题,该方法科学可行、可视性强、准确率高,应用前景广阔。
Referring to the surrounding rock classification,a method based on data mining technology and also the support vector machine and binary tree was proposed.It chose 9 leading indexes which affected the classification of surrounding rock,used the method of binary tree support vector machine to classify and predict surrounding rock data,made the best of SPSS Modeler data mining tool to build the classification fitting models and achieved intelligent decision output as well.The results show that the method of classification support vector machine and surrounding rocks which based on data mining can provide a good solution to the problem of small sample,non-linear and high dimension.This method is scientific and feasible and also has high accuracy,which leads it to a broad application prospect.
出处
《人民黄河》
CAS
北大核心
2017年第7期135-138,共4页
Yellow River
基金
国家杰出青年科学基金资助项目(41225011)
高等学校博士学科点专项科研基金(优先发展领域)资助项目(20135122130002)
关键词
围岩
分类
支持向量机
数据挖掘
二叉树
surrounding rock
classification
support vector machine
data mining
binary tree
