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- Title
An Empirical Investigation of Filter Attribute Selection Techniques for High-Speed Network Traffic Flow Classification.
- Authors
Yang, Jie; Ma, Jing; Cheng, Gang; Wang, Yixuan; Yuan, Lun; Dong, Chao
- Abstract
Attribute selection is an important methodology for data mining problems. Removing irrelevant and redundant attributes from original data set can greatly simplify building classifier models. In this paper, we consider applying attribute selection techniques to network traffic flow classification and conduct experiments using the actual network data collected from the Internet of China. The results show that building with an appropriate attribute selection method can simplify the network traffic classifier while achieving satisfactory classification accuracy.
- Subjects
CHINA; EMPIRICAL research; CONTENT filters (Computer science); COMPUTER simulation of traffic flow; DATA mining; ACQUISITION of data
- Publication
Wireless Personal Communications, 2012, Vol 66, Issue 3, p541
- ISSN
0929-6212
- Publication type
Article
- DOI
10.1007/s11277-012-0735-y