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Construction of Customer Classification Model Based on Bayesian Network

机译：基于贝叶斯网络的客户分类模型构建

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—At present, the researches on customer segmentation model based on Bayesian network are few. This paper makes a research on the classification problems based on Bayesian network. First of all, it used literature search and case study to describe the related knowledge and classification principles on Bayesian network. After that, combining with Adventure Works Cycles company's customer data, we made the use of K2 learning algorithm to search the best network structure and got two more reasonable Bayesian network topologies. Thereafter, we calculated the posterior probability and selected the largest one of Bayesian network. Then, we adopted 10 - fold stratified cross-validation method to verify the correctness of classification model, and the results are satisfactory. Finally, this paper finds out Bayesian network classification has greater advantages than other classification methods.
机译：-AT存在，基于贝叶斯网络的客户分割模型的研究很少。本文对基于贝叶斯网络的分类问题进行了研究。首先，它使用文献搜索和案例研究来描述贝叶斯网络的相关知识和分类原则。之后，结合冒险工作周期公司的客户数据，我们使用K2学习算法搜索了最佳的网络结构并获得了两个更合理的贝叶斯网络拓扑。此后，我们计算了后验概率，并选择了贝叶斯网络中最大的概率。然后，我们采用了10倍的分层交叉验证方法来验证分类模型的正确性，结果令人满意。最后，本文发现贝叶斯网络分类具有比其他分类方法更大的优势。

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