An Optimized Feature Subset Selection Method for Network Flows based on Machine Learning

DOI

10.2991/icmse-18.2018.134How to use a DOI?

Keywords

Network flows classification; Feature subset selection; Machine learning; Dynamic block.

Abstract

As the foundation of network cognition, management and optimizing, the classification of network traffic is making a significant difference in resource scheduling, safety analysis and future tendency prediction. Feature subset selection (FSS) based on machine learning plays an important role in classification problems, especially dealing with high-dimensional data like network traffic flows. To realize accurate traffic classification at lower price of evaluations, a hybrid feature subset selection method is proposed on the base of dynamic block, the size of which is flexible according to the classification performance. The performances are examined a few experiments. Our theoretical analysis and experimental observations reveal that the proposed method consumes fewer evaluations with similar or even better classification performance.

Copyright

© 2018, 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  - Xiaoyan Zhang
AU  - Jingbo Xia
AU  - Ruixin Li
PY  - 2018/05
DA  - 2018/05
TI  - An Optimized Feature Subset Selection Method for Network Flows based on Machine Learning
BT  - Proceedings of the 2018 8th International Conference on Manufacturing Science and Engineering (ICMSE 2018)
PB  - Atlantis Press
SP  - 730
EP  - 735
SN  - 2352-5401
UR  - https://doi.org/10.2991/icmse-18.2018.134
DO  - 10.2991/icmse-18.2018.134
ID  - Zhang2018/05
ER  -