Internet traffic classification using SVM with flexible feature space
SVM is a typical machine learning algorithm with prefect generalization capacity,which is suitable for the internet traffic classification.At present,there are two approaches,One-Against-All and One-Against-One,proposed for extending SVM to multi-class problem like traffic classification.However,the...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Beijing Xintong Media Co., Ltd
2016-05-01
|
| Series: | Dianxin kexue |
| Subjects: | |
| Online Access: | http://www.telecomsci.com/thesisDetails#10.11959/j.issn.1000-0801.2016132 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | SVM is a typical machine learning algorithm with prefect generalization capacity,which is suitable for the internet traffic classification.At present,there are two approaches,One-Against-All and One-Against-One,proposed for extending SVM to multi-class problem like traffic classification.However,these approaches are both based on a unique feature space.In fact,the separating capacity of a special traffic feature is not similar to different applications.Hence,flexible feature space for extending SVM was proposed,which constructs independent feature space with optimal discriminability for each binary-SVM and trains them under their own feature space.Finally,these trained binary-SVM were ensemble by One-Against-All and One-Against-One approaches.The experiments show that the proposed approach can efficiently improve the precision and callback of the traffic classifier and easily obtain more reasonable optimal separating hyper-plane. |
|---|---|
| ISSN: | 1000-0801 |