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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. [ABSTRACT FROM AUTHOR]

Abstract: Preventing customer churn is an important task for many enterprises and requires customer churn prediction. This paper investigates the effects of interpersonal influence on the accuracy of customer churn predictions and proposes a novel prediction model that is based on interpersonal influence and that combines the propagation process and customers’ personalized characters. Our contributions include the following: (1) the effects of interpersonal influence on prediction accuracy are evaluated while including determinants that other researchers proved effective, and several models are constructed based on machine learning and statistical methods and compared, assuring the validity of the evaluation; and (2) a novel prediction model based on interpersonal influence and information propagation is proposed. The dataset used in the empirical study was obtained from a leading mobile telecommunication service provider and contains the traditional and network attributes of over one million customers. The empirical results show that traditional classification models that incorporate interpersonal influence can greatly improve prediction accuracy, and our proposed prediction model outperforms the traditional models. [Copyright &y& Elsevier]