Fault Prediction for Network Equipment based on Oversampling Method in Imbalanced Dataset

Nowadays, there are two main problems in Network service. On one hand, Network customers require better service and experience. For another, the Network service providers prefer to improve users' quality of experience (QoE) in order to increase their revenues. In this paper, we combine the network parameters and user complaints to establish a fault prediction model. Firstly, we clean the data and use an improved SMOTE algorithm called K-SMOTE to balance the data. Then we use principal component analysis (PCA) to perform dimensionality reduction on the data, which is beneficial to algorithm efficiency. Finally, in order to get a more accurate model, we conduct our experiment based on some machine learning models in our dataset. Experimental results show that decision tree model using K-SMOTE algorithm has a better performance than other models.