关键词: 客户流失, 多层感知机, 离散属性, 堆叠自编码器, 实体嵌入, 映射

Abstract:

To deal with the issues of the increasing data attributes and sparse data, evoked by using one-hot encoding method to encode discrete properties, in the preprocessing of customer churn prediction, this paper proposes two improved customer churn prediction models based on multi-layer perceptron. The main idea is to improve multi-layer perceptron by using stacked auto-encoder and entity embedding respectively. By mapping the high dimensional data of discrete properties into low dimensional space, the methods can reduce the number of sparse data made by one-hot encoding and increase the correlation between different values of discrete properties efficiently. The cross-validation results testing on two public data sets reveal that the improved methods not only increase the accuracy of prediction efficiently but also keep the advantage of traditional multi-layer perceptron in parallel computing.

Key words: customer churn, multi-layer perceptron, discrete attributes, auto-encoder, entity embedding, map