基于贝叶斯分类模型的保险赔付问题研究

The Study of the Insurance Payments on Bayesian Classification Model

10.6338/JDA.200810_3(5).0008

王旭(Xu Wang)；刘冬(Dong Liu)；石磊(Lei Shi)；朱建旭(Jian-Xu Zhu)；李扬(Yang Li)

保险业 ； 赔付 ； 贝叶斯网络 ； 数据挖掘 ； insurance industry ； payment ； Bayes Network ； data mining

Journal of Data Analysis

3卷5期（2008 / 10 / 01）

109 - 123

簡體中文

本文将贝叶斯分类器作为主要工具，根据相应的人群属性来对保险赔付情况进行研究。首先用K2算法训练贝叶斯网络模型，并将模型预测结果与传统分类器（如cart、logistic回归等）及朴素贝叶斯进行比较。鉴于K2算法自身存在的一些缺陷，本文进一步提出了基于关联规则的贝叶斯网络模型，并通过学习得到了预测能力良好且较为鲁棒的保险赔付测算模型。

The purpose of this article is to explore, by Bayes Classifier, how the insurance payments perform in the light of the reciprocal attributes of the people involved in the given situation. At first, we use the k2 algorithm to train the Bayes Network model, and compare the results with those of the traditional taxonomy (such as cart, logistic) and the Naive Bayes model. Because of the limitations of k2, however, we put forward and adopt the Bayes Network model on the basis of associate rule, resulting in a model with better robustness, which also guarantees the capability of forecasting.

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