GTC Forest: An Ensemble Method for Network Structured Data Classification

Abstract

In recent years, deep neural networks have achieved great success in various applications, particularly in visual tasks such as image classification. However, deep neural networks cannot reach their full potential when dealing with classification problems in networks. Because the network-related dataset is usually in a structured format rather than an image format, and in some cases the data scale is small to train a deep model. Therefore, we aim at another choice, which can abandon the structure of deep neural networks, but remain the powerful representation learning ability. In this paper, we propose GTC Forest, a tree-based ensemble method for network structured data classification. GTC Forest consists of two parts: the first part Multi-Grained Traversing to do representation learning in network structured data; and the second part Cascade Forest to train on small-scale dataset, as well as reducing model complexity. Experiments are conducted on user broadband dataset, which is built to guarantee users a better Internet experience. And the results prove that our model is effective, and has higher accuracy than other machine learning methods in network structured data classification.