Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1145/1815396.1815569 | IWCMC |
Keywords | Field | DocType |
custom probabilistic graphical model,traffic classification problem,semi-supervised traffic classification,feature selection problem,traffic classification,machine learning,active research area,unlabelled traffic flow,semi-supervised learning,problem-specific solution,graphical model,probabilistic graphical models,feature selection,traffic flow,semi supervised learning | Traffic classification,Semi-supervised learning,Feature selection,Computer science,Artificial intelligence,Probabilistic logic,Graphical model,Modular design,Machine learning | Conference |
Citations | PageRank | References |
7 | 0.56 | 10 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Charalampos Rotsos | 1 | 314 | 21.95 |
| Jurgen Van Gael | 2 | 426 | 25.02 |
| Andrew Moore | 3 | 7647 | 894.46 |
| Zoubin Ghahramani | 4 | 10455 | 1264.39 |