在线游戏用户的流失预测:基于不平衡数据的采样方法比较和分析

User Churn Prediction for Online Game:Comparison and Analysis of Approaches Based on Sampling for Imbalanced Data

流失用户预测问题在很多领域都是研究重点。目前主流的流失用户预测方法是使用分类法,即把用户是否会流失看作一个二分类问题来处理。该文提出了一个基于二分类问题解决的在线游戏流失用户预测方法。此方法除了总结了一些对在线游戏而言比较重要的可以用于流失预测的特征之外,也考虑到流失用户相对稀少的问题,在流失用户预测问题中引入了不平衡数据分类的思想。该文主要在流失预测中结合使用了基于采样法的不平衡数据处理策略,并对现有主要的几种采样算法进行了对比实验和分析。

The problem of user churn prediction is a research focus in many fields.Currently the main approach of the problem is based on classification,which predicts whether users will churn by a 2-class classification process.This paper addresses an approach for online game user churn prediction based on 2-class classification.We summarize some important features for the problem of online game user churn prediction.Furthermore,we noticed that churned users is relatively rare,and introduce the imbalanced learning methods into our work with a focus on the sampling methods.We conducted experiments on major sampling methods and analyzed the results.