关键词: 迁移学习, 视频业务, 网络流分类

Abstract:

In the real world, the available training data is usually small and easily outdated, so a large number of new data sets need to be continuously collected and labelled. To address this problem, a transfer learning classification method based on SAMME and TrAdaBoost algorithms is proposed. The core idea of the method is to filter useful samples from the old data set to help the model identify new video samples, where the feature distributions of the old and new video traffic datasets are different. At the same time, this method combines the SAMME algorithm to extend the TrAdaBoost algorithm from only two classifications to multi-classification. Experimental results show that the proposed method can better achieve fine-grained classification of six types of video traffic and reduce the waste of a large number of old data sets compared to existing methods.

Key words: transfer learning, video services, network traffic classification