Rotsos, C and Van Gael, J and Moore, AW and Ghahramani, Z (2010) Probabilistic graphical models for semi-supervised traffic classification. In: UNSPECIFIED pp. 752-757..Full text not available from this repository.
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. Copyright © 2010 ACM.
|Item Type:||Conference or Workshop Item (UNSPECIFIED)|
|Divisions:||Div F > Computational and Biological Learning|
|Depositing User:||Cron Job|
|Date Deposited:||09 Dec 2016 17:28|
|Last Modified:||22 Jan 2017 01:57|