Reliable Probabilistic Classification and Its Application to Internet Traffic

Authors:
Mikhail Dashevskiy;Zhiyuan Luo
Affiliations:
Computer Learning Research Centre Royal Holloway, University of London, UK TW20 0EX;Computer Learning Research Centre Royal Holloway, University of London, UK TW20 0EX
Venue:
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Theoretical and Methodological Issues
Year:
2008

Citing 7
Cited 1

Efficient Feature Selection via Analysis of Relevance and Redundancy

The Journal of Machine Learning Research
Algorithmic Learning in a Random World

Algorithmic Learning in a Random World
Internet traffic classification using bayesian analysis techniques

SIGMETRICS '05 Proceedings of the 2005 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Automated Traffic Classification and Application Identification using Machine Learning

LCN '05 Proceedings of the The IEEE Conference on Local Computer Networks 30th Anniversary
A preliminary performance comparison of five machine learning algorithms for practical IP traffic flow classification

ACM SIGCOMM Computer Communication Review
Traffic classification using a statistical approach

PAM'05 Proceedings of the 6th international conference on Passive and Active Network Measurement
Bayesian Neural Networks for Internet Traffic Classification

IEEE Transactions on Neural Networks

Reliable probabilistic classification with neural networks

Neurocomputing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Many machine learning algorithms have been used to classify network traffic flows with good performance, but without information about the reliability in classifications. In this paper, we present a recently developed algorithmic framework, namely the Venn Probability Machine, for making reliable decisions under uncertainty. Experiments on publicly available real traffic datasets show the algorithmic framework works well. Comparison is also made to the published results.