Probabilistic graphical models for semi-supervised traffic classification
Probabilistic graphical models for semi-supervised traffic classification
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DOI：
10.1145/1815396.1815569
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发表时间：
2010-06
期刊：
影响因子：
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通讯作者：
Charalampos Rotsos;Jurgen Van Gael;A. Moore;Zoubin Ghahramani
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文献类型：
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作者：
Charalampos Rotsos;Jurgen Van Gael;A. Moore;Zoubin Ghahramani
使用机器学习的交通分类仍然是一个活跃的研究领域。该领域的大多数工作都使用现成的机器学习工具，并将其视为黑盒分类器。这种方法将所有建模复杂性变成特征选择问题。在本文中，我们通过设计自定义概率图形模型来为流量分类问题构建特定问题的解决方案。图形模型是设计分类器的模块化框架，该分类器包含特定领域的知识。更具体地说，我们的解决方案引入了半监督学习，这意味着我们从标记和未标记的交通流中学习。我们表明，与以前的方法相比，我们的解决方案的性能是竞争性的，同时使用较少的数据和更简单的功能。
Traffic classification using machine learning continues to be an active research area. The majority of work in this area uses off-the-shelf machine learning tools and treats them as black-box classifiers. This approach turns all the modelling complexity into a feature selection problem. In this paper, we build a problem-specific solution to the traffic classification problem by designing a custom probabilistic graphical model. Graphical models are a modular framework to design classifiers which incorporate domain-specific knowledge. More specifically, our solution introduces semi-supervised learning which means we learn from both labelled and unlabelled traffic flows. We show that our solution performs competitively compared to previous approaches while using less data and simpler features.