%0 Journal Article %A XIA Guo’en %A TANG Qi %A ZHANG Xianquan %T Improved Multi-layer Perceptron Applied to Customer Churn Prediction %D 2020 %R 10.3778/j.issn.1002-8331.1904-0496 %J Computer Engineering and Applications %P 257-263 %V 56 %N 14 %X

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.

%U http://cea.ceaj.org/EN/10.3778/j.issn.1002-8331.1904-0496