Gradient descent evolved imbalanced data gravitation classification with an application on Internet video traffic identification

Abstract In the last decade, the increasing video traffic, especially illegal videos brought big challenges for Internet management. Generally, abnormal videos, such as illegal videos only account for a small percentage which makes the detection of such videos to be a typical imbalanced classification problem. In this study, we propose a new imbalanced learning method, namely, the imbalanced data gravitation classification model based the gradient descent (IDGC-GD), to handle imbalanced problems. In IDGC-GD model, we use the gradient descent algorithm to optimize feature weights of the imbalanced data gravitation classification (IDGC) model. Then, we try to build an accurate video traffic identification solution using IDGC-GD. We conduct a set of comparing experiments between IDGC-GD and seven imbalanced learning algorithms using 21 open data sets and four video traffic data sets collected from the real application. Experimental results show that our method is promising for solving imbalanced problems, including Internet video traffic identification.