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...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | zho |
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Beijing Xintong Media Co., Ltd
2016-05-01
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| Series: | Dianxin kexue |
| Subjects: | |
| Online Access: | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2016132/ |
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| _version_ | 1841529871017181184 |
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| author | Yaguan QIAN Xiaohui GUAN Bensheng YUN Qiong LOU Pengfei MA |
| author_facet | Yaguan QIAN Xiaohui GUAN Bensheng YUN Qiong LOU Pengfei MA |
| author_sort | Yaguan QIAN |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-3d23014c23db45d3a4720dcf5092187a |
| institution | Kabale University |
| issn | 1000-0801 |
| language | zho |
| publishDate | 2016-05-01 |
| publisher | Beijing Xintong Media Co., Ltd |
| record_format | Article |
| series | Dianxin kexue |
| spelling | doaj-art-3d23014c23db45d3a4720dcf5092187a2025-01-15T03:14:54ZzhoBeijing Xintong Media Co., LtdDianxin kexue1000-08012016-05-013210511359608893Internet traffic classification using SVM with flexible feature spaceYaguan QIANXiaohui GUANBensheng YUNQiong LOUPengfei MASVM 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.http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2016132/support vector machineflexible feature spacetraffic classification |
| spellingShingle | Yaguan QIAN Xiaohui GUAN Bensheng YUN Qiong LOU Pengfei MA Internet traffic classification using SVM with flexible feature space Dianxin kexue support vector machine flexible feature space traffic classification |
| title | Internet traffic classification using SVM with flexible feature space |
| title_full | Internet traffic classification using SVM with flexible feature space |
| title_fullStr | Internet traffic classification using SVM with flexible feature space |
| title_full_unstemmed | Internet traffic classification using SVM with flexible feature space |
| title_short | Internet traffic classification using SVM with flexible feature space |
| title_sort | internet traffic classification using svm with flexible feature space |
| topic | support vector machine flexible feature space traffic classification |
| url | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2016132/ |
| work_keys_str_mv | AT yaguanqian internettrafficclassificationusingsvmwithflexiblefeaturespace AT xiaohuiguan internettrafficclassificationusingsvmwithflexiblefeaturespace AT benshengyun internettrafficclassificationusingsvmwithflexiblefeaturespace AT qionglou internettrafficclassificationusingsvmwithflexiblefeaturespace AT pengfeima internettrafficclassificationusingsvmwithflexiblefeaturespace |