Unbiased Decision Tree Model for User's QoE in Imbalanced Dataset
Lei Wang, Jiefeng Jin, Ruochen Huang, Xin Wei, Jianxin Chen · 2016
Nowadays, Internet Protocol Television (IPTV) is gradually replacing the traditional TV. IPTV Users require better experience. Therefore, media providers are interested in finding the key factors which influence the Quality of Experience (QoE), and it is necessary to find a model to predict the QoE. In this paper, we discuss the relationship between the status of IPTV set-top box and user's QoE. There is not a uniform standard to measure or improve user's QoE in IPTV, so we combine the status data from IPTV set-top box with user's complaints, selecting the appropriate model and using it for predicting user's QoE. As the data from IPTV set-top box is imbalance, the traditional algorithm does not perform well in terms of predicting user's QoE. To solve this problem, we propose the unbiased decision tree model to deal with the imbalance dataset. First of all, we clean the dataset. Then, we select important features influencing QoE by the feature selection technology. Finally, we compare CART model and the unbiased decision tree model. We demonstrate that the unbiased decision tree model performs well in the imbalance dataset and achieve a high accuracy.