CHAID-RF:基于CHAID决策树的集成学习方法
CHAID-RF:Ensemble Learning Method Based on CHAID Decision Tree
聂斌 1靳海科 1李欢 1陈裕凤 1张玉超 1郑学鹏1
作者信息
- 1. 江西中医药大学 计算机学院,江西 南昌 330004
- 折叠
摘要
针对卡方自动交互诊断(CHAID)决策树易过拟合的问题,提出CHAID随机森林方法(CHAID Random Forest,CHAID-RF).该方法采用随机采样、随机选择特征以及集成的策略,将CHAID决策树作为基分类器,形成CHAID-RF.为了验证CHAID-RF的有效性,选取CART、CHAID、SVM、RF作为对比算法,以准确率、加权查准率、加权查全率、加权F值作为分类模型评价指标,以均方根误差作为回归模型评价指标,采用 10 个分类数据集和 7 个回归数据集进行验证.实验结果表明CHAID-RF可行有效.
Abstract
Aiming at the problem that CHAID Decision Tree is easy to overfitting,CHAID-RF is proposed.In this method,CHAID Decision Tree is used as the base classification to form CHAID-RF by random sampling,random feature selection and integration strategies.CART,CHAID,SVM,and RF are selected as the comparison algorithm to verify the effectiveness of CHAID-RF,accuracy,Weighted Precision Ratio,Weighted Recall Ratio,and Weighted F-measure are used as evaluation index of classification model,and Root Mean Square Error is used as evaluation index of regression model,10 classification data sets and 7 regression data sets are used for validation.The experimental results show that CHAID-RF is feasible and effective.
关键词
CHAID/随机森林/CHAID-RF/分类/回归Key words
CHAID/Random Forest/CHAID-RF/classification/regression引用本文复制引用
出版年
2024
国家科技期刊平台
NSTL
万方数据