| 摘要: |
| 由于以往的网络流量分类方法是单一的机器学习分类方法，这种方法的总体准确率（Overall Accuracy）提高困难，而且这个问题长期存在着，鉴于此，提出了一种新的网络流量分类的方法，以机器学习分类方法为基础，联合不同分类方法，运用集成学习的思想，使用加权组合权重的方式来实现网络流量的分类；实验表明，新方法提高了总体准确率，比单一的机器学习分类方法更好。 |
| 关键词: 流量分类 支持向量机 贝叶斯增广朴素贝叶斯 BP神经网络 集成学习 |
| DOI： |
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| Network Traffic Classification Based on the Combination of Multi classifiers |
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GU Yue1, TANG Xue wen2
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| Abstract: |
| According to network traffic classification, because the previous network traffic classification method is single machine learning classification, it is difficult to improve its Overall Accuracy and this problem exists for a long time. Therefore, this paper proposes a new network traffic classification method, this method is based on machine learning classification, combines the advantages of different classification methods, uses integrated learning, and obtains the classification of network traffic through weighted average combination. The experiment shows that this method raises Overall Accuracy and is better than single machine learning classification. |
| Key words: traffic classification support vector machine Bayes Network Augmented Naive Bayses BP neural network ensemble learning |
