A stable feature selection approach for optimizing traffic classification based on adaptive threshold

DOI

10.2991/icence-16.2016.152How to use a DOI?

Keywords

Traffic classification, Feature selection, ATFS, TRF, WSU.

Abstract

In recent years, machine learning algorithm has been widely studied in the field of traffic classification. However, most studies focus on performance improvement of classifier, pro-phase work of traffic classification - feature selection is ignored. Therefore, WSU is regarded as metric, an ATFS algorithm - (Adaptive threshold feature select) is designed on the basis. Namely, algorithm is based on precision autonomous selection threshold of classifier aiming at different datasets. Each dataset will generate a set of attribute subset eventually. Stable features are selected in different screened attribute subsets through TRF algorithm, thereby reaching the purpose of high precision. The experiment shows that the features finally selected in the algorithm can reach the precision of >96% on C4.5 classifier.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Wenbei Duan
AU  - Yuanli Wang
PY  - 2016/09
DA  - 2016/09
TI  - A stable feature selection approach for optimizing traffic classification based on adaptive threshold
BT  - Proceedings of the 2nd International Conference on Electronics, Network and Computer Engineering (ICENCE 2016)
PB  - Atlantis Press
SP  - 827
EP  - 832
SN  - 2352-538X
UR  - https://doi.org/10.2991/icence-16.2016.152
DO  - 10.2991/icence-16.2016.152
ID  - Duan2016/09
ER  -