CHURN PREDICTION WITH LINEAR DISCRIMINANT BOOSTING ALGORITHM

摘要：

In this paper, a novel classification algorithm called Linear Discriminant Boosting (LD-Boosting) is proposed. By aggregating LOA learning through the boosting framework, this algorithm can deal with complicated binary classification problems, especially problems such as churn prediction with extremely imbalanced dataset. LD-Boosting is efficient since the most discriminative feature is computed in closed form in each iteration, with neither time-consuming numerical optimization nor exhaustive search. Furthermore, because of the computational simplicity of LDA learning, the method is able to utilize huge amount of training samples efficiently. In addition, boosting technique is employed in this algorithm to put heavier penalties on misclassification of the minority class, therefore directly reduces error cases and achieves more precise prediction results. The effectiveness of the proposed algorithm is validated by churn prediction experiments on a real bank customer churn data set. The method is found to improve prediction accuracy significantly compared with other algorithms, such as artificial neural networks, decision trees, support vector machines, and classical AdaBoost algorithm.

关键词： Churn prediction linear discriminant analysis boosting

作者: YAYA XIE XIU LI

作者单位: Department of Automation, Tsinghua University, Beijing, P.R.China

会议类型: 国际会议

会议名称: 2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)

会议地点: 昆明

会议语种:英文

页码: 228-233

在线出版日期: 2008-07-12（万方平台首次上网日期，不代表论文的发表时间）