现代信息科技2024，Vol.8Issue(17)：28-35,42,9.DOI:10.19850/j.cnki.2096-4706.2024.17.007
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
摘要
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分类
信息技术与安全科学引用本文复制引用
聂斌,靳海科,李欢,陈裕凤,张玉超,郑学鹏..CHAID-RF:基于CHAID决策树的集成学习方法[J].现代信息科技,2024,8(17):28-35,42,9.基金项目
国家自然科学基金项目(82260849,61562045) （82260849,61562045）
江西省教育厅科技计划研究项目(GJJ211256) （GJJ211256）
江西中医药大学校级科技创新团队发展计划(CXTD22015) （CXTD22015）
