ABSTRACT Machine learning based mobile traffic classification has become a popular topic in recent years. As mobile traffic data is dynamic in nature, the static model has become ineffective for the task of classifying future traffic. This is known as the concept drift problem in data streams. To this end, this paper presents an adaptive mobile traffic classification method. Specifically, a method based on the fuzzy competence model is devised to detect concept drift, and a dynamic learning method is presented to update the classification model, so as to adapt to an ever-changing environment at an appropriate time. The concept drift detection method relies on the data distribution instead of the classification error rate. Furthermore, the weights of flow samples are dynamically updated and flow samples are resampled for training a new model when a concept drift is detected. Moreover, recently trained models are saved and used for classification in weighted voting. The weight of each model is updated according to the performance it obtains on the most recent flow samples. On mobile traffic data, experimental results show that our proposed method obtains lower classification error rate with less time consumption on updating models as compared to related methods designed for handling concept drift problems.

中文翻译：

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移动网络流量数据自适应学习

摘要 基于机器学习的移动流量分类已成为近年来的热门话题。由于移动流量数据本质上是动态的，静态模型对于未来流量分类任务变得无效。这被称为数据流中的概念漂移问题。为此，本文提出了一种自适应移动流量分类方法。具体而言，设计了一种基于模糊能力模型的方法来检测概念漂移，并提出一种动态学习方法来更新分类模型，以便在适当的时候适应不断变化的环境。概念漂移检测方法依赖于数据分布而不是分类错误率。此外，当检测到概念漂移时，流样本的权重被动态更新，流样本被重新采样以训练新模型。此外，最近训练的模型被保存并用于加权投票的分类。每个模型的权重根据它在最近的流样本上获得的性能进行更新。在移动交通数据上，实验结果表明，与为处理概念漂移问题而设计的相关方法相比，我们提出的方法在更新模型上花费的时间更少，获得了更低的分类错误率。