An Incremental Feature Subset Selection Strategy Based on Convergency

DOI

10.2991/caai-17.2017.94How to use a DOI?

Keywords

network traffic classification; feature subset selection; incremental strategy

Abstract

Traffic classification is currently a significant challenge for network monitoring and management. Feature selection based on machine learning is an effective method to realize dimension reduction and decrease redundant information. To classify traffic flows with better performance, we put forward the incremental strategy of convergence. The strategy gathers all the features that have been selected and adds an extra round of selection on the base of the original algorithm to discover the value of relationship among all the selected features. The performances are examined by experiment. Our theoretical analysis and experimental observations reveal that the proposed incremental strategy of convergence makes a further improvement on the classification accuracy.

Copyright

© 2017, 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  - Jian Shen
AU  - Jingbo Xia
AU  - Haiou Shen
PY  - 2017/06
DA  - 2017/06
TI  - An Incremental Feature Subset Selection Strategy Based on Convergency
BT  - Proceedings of the 2017 2nd International Conference on Control, Automation and Artificial Intelligence (CAAI 2017)
PB  - Atlantis Press
SP  - 415
EP  - 418
SN  - 1951-6851
UR  - https://doi.org/10.2991/caai-17.2017.94
DO  - 10.2991/caai-17.2017.94
ID  - Shen2017/06
ER  -