论文标题
社交网络分类器的比较研究，用于预测电信行业的流失
A Comparative Study of Social Network Classifiers for Predicting Churn in the Telecommunication Industry
论文作者
论文摘要
网络数据中的关系学习已被证明在许多研究中有效。关系学习者由关系分类器和集体推理方法组成，鉴于与其他节点的链接的存在和强度，可以推断网络中的节点。这些方法已被调整为预测电信公司中的客户流失，表明将它们纳入其中可能会提供更准确的预测。在这项研究中，通过将各种关系学习者的表现应用于源自电信行业的许多CDR数据集，其目标是将它们整体排名并研究关系分类器和集体推理方法的效果。我们的结果表明，集体推理方法并不能提高关系分类器的性能，并且最佳性能的关系分类器是基于网络的基于网络的分类器，该分类器使用基于链接的网络节点进行基于链接的测量方法来构建逻辑模型。
Relational learning in networked data has been shown to be effective in a number of studies. Relational learners, composed of relational classifiers and collective inference methods, enable the inference of nodes in a network given the existence and strength of links to other nodes. These methods have been adapted to predict customer churn in telecommunication companies showing that incorporating them may give more accurate predictions. In this research, the performance of a variety of relational learners is compared by applying them to a number of CDR datasets originating from the telecommunication industry, with the goal to rank them as a whole and investigate the effects of relational classifiers and collective inference methods separately. Our results show that collective inference methods do not improve the performance of relational classifiers and the best performing relational classifier is the network-only link-based classifier, which builds a logistic model using link-based measures for the nodes in the network.
