Comparative Research of Hyper-Parameters Mathematical Optimization Algorithms for Automatic Machine Learning in New Generation Mobile Network

被引:1
|
作者
Zhang, Xiaohang [1 ]
Li, Yuqi [1 ]
Li, Zhengren [2 ]
机构
[1] Beijing Univ Posts & Telecommun, Sch Econ & Management, Beijing 100876, Peoples R China
[2] Beijing Univ Posts & Telecommun, Sch Modern Post, Beijing 100876, Peoples R China
来源
MOBILE NETWORKS & APPLICATIONS | 2022年 / 27卷 / 03期
关键词
Machine learning; Automatic algorithm selection; Hyper-parameter optimization; 5G; New generation mobile network; Information technology;
D O I
10.1007/s11036-022-01913-x
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
Under the configuration of the new generation communication network, the algorithm based on machine learning has been widely used in network optimization and mobile user behavior prediction. Therefore, the optimization method with hyper-parameters will have a huge development space in the field of mobile communication network. However, for non-professionals, the bottleneck that restricts the further development and application of the whole machine learning is the selection of suitable machine learning algorithm and the determination of suitable algorithm hyper-parameters. Researchers have proposed to use automatic machine learning algorithm to solve this remarkable problem. This article forms a technical manual that can be easily searched by researchers with summarizing related hyper-parameter optimization methods and proposing the corresponding algorithm framework. Moreover, through the comparison of related optimization methods, we highlight the characteristics and deficiencies of related algorithms in the new generation of mobile networks, and put forward suggestions for future improvement.
引用
收藏
页码:928 / 935
页数:8
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