Open AccessResearch on Traffic Classification of Auto Machine-learning Base on Meta-QNN
Author(s) -
Xin Xu,
Yong Huang,
Yong Luo,
Guige Ouyang,
Haoyu Zhuang
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1757/1/012006
Subject(s) - traffic classification , machine learning , computer science , artificial intelligence , architecture , artificial neural network , task (project management) , feature engineering , representation (politics) , deep learning , feature (linguistics) , engineering , the internet , art , linguistics , philosophy , systems engineering , politics , world wide web , law , political science , visual arts
The traditional traffic classification task relies on feature engineering by experts with specialized knowledge. With the advent of representation-learn-based traffic classification techniques, machines learned to extract features from traffic data and classify them. In this paper, we research traffic classification technology based on representational learning and import auto-machine learning technology to solve the problems of network architecture design and parameter tuning. We also design a befitting reward function for the network architecture model. The experimental results on the USTC-TF2016 dataset and USTC-TF2016-PLUS shows that the network architecture generated by auto-machine learning technology has better training performance and classification accuracy than a traditional neural network.