Taxonomy of machine learning algorithms to classify realtime interactive applications
The needs of Internet applications QoS guarantee increased the demand of internet traffic classification, especially for interactive real time applications. Therefore, several classification methods were developed. Machine Learning (ML) classification is one of the most modern techniques, which sol...
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| Main Authors: | , , , |
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| Format: | Article |
| Published: |
International Research Association of Computer Science, & Technology (IRACST)
2012
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| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/33568/ https://www.iracst.org/ijcnwc/papers/vol2no12012/13vol2no1.pdf |
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| Institution: | Universiti Teknologi Malaysia |
| Summary: | The needs of Internet applications QoS guarantee increased the demand of internet traffic classification, especially for interactive real time applications. Therefore, several classification methods were developed. Machine Learning (ML) classification is one of the most modern techniques, which solves the problem of traditional port base method. This paper compared experimentally the accuracy of ten ML algorithms, that when it’s used to classify interactive applications. The technique a pplied by collecting of real data from UTM. The result shows that Tree.RandomForest algorithm provided optimal results of 99.8% accuracy, compared with other algorithms. |
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