摘要
为促进农村商业医疗保险的发展,提出了一种基于k-近邻算法、决策树算法和逻辑回归算法的kDT-LR融合模型,动态地为集成中的每个学习器分配有效能力,并根据CGSS2017家户调查数据,构建农村商业医疗保险潜在客户识别模型。结果表明,k-DT-LR融合模型算法的分类准确率达到90.024%,召回率达到91.402%,能够精确地识别出农村商业医疗保险潜在客户。
In order to promote the development of rural commercial medical insurance,the k-DT-LR fusion model based on k-nearest neighbor algorithm,decision tree algorithm and logical regression algorithm was proposed to dynamically allocate effective capabilities to each learner in the integration.Using CGSS2017 household survey data,the potential customer identification model of rural commercial medical insurance was constructed.The experimental results showed that the classification accuracy of k-DT-LR algorithm was 90.024%and the recall rate was 91.402%,which could accurately identify the potential customers of rural commercial medical insurance.
作者
周可心
袁永生
林春进
ZHOU Ke-xin;YUAN Yong-sheng;LIN Chun-jin(School of Science,Hohai University,Nanjing 211100,China)
出处
《湖北农业科学》
2022年第24期144-148,共5页
Hubei Agricultural Sciences
基金
国家自然科学基金项目(11201116)。
关键词
农村商业医疗保险
K近邻算法
决策树算法
逻辑回归算法
集成学习
潜在客户识别
rural commercial medical insurance
k-nearest neighbor algorithm
decision tree algorithm
logistic regression algorithm
integrated learning
potential customer identification
