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.
CHAID-RF:Ensemble Learning Method Based on CHAID Decision Tree
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.
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