摘要
针对传统的人工方式判断车主是否有意向购买车险存在效率低、缺乏预测性等问题,提出一种基于支持向量机的车险购买意向识别方法。首先,通过标准化处理与主成分分析降维,将30维的通话数据映射至10维空间,并采用欠抽样策略解决数据样本不平衡的问题;然后,利用SVM模型区分有、无意向车主。实验结果表明,SNM模型的识别召回率和误检率分别为97.9%、4.3%。该方法可为车险公司个性化服务提供技术支持。
Aiming at the problems of low efficiency and lack of predictability in traditional manual methods of judging whether car owners intend to purchase car insurance,a car insurance purchase intention recognition method based on support vector machine is proposed.Firstly,through standardization and principal component analysis dimensionality reduction,the 30 dimensional call data is mapped to a 10 dimensional space,and under sampling strategy is adopted to solve the problem of imbalanced data samples;Then,use SVM model to distinguish between interested and uninterested car owners.The experimental results show that the recognition recall rate and false detection rate of the SNM model are 97.9%and 4.3%.This method can provide technical support for personalized services of car insurance companies.
作者
邵延富
谢大为
SHAO Yanfu;XIE Dawei(Guangzhou Joysim Technology Co.,Ltd.,Guangzhou 510000,China)
出处
《自动化与信息工程》
2024年第6期87-92,共6页
Automation & Information Engineering
基金
广东省科技计划项目(2016A050502060,2020B1010010005)
广州市科技计划项目(202206010011,2023B03J1339)。
关键词
车险购买意向识别
主成分分析
支持向量机
通话数据
car insurance purchase intention recognition
principal component analysis
support vector machine
calling data
