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Network Service Analysis Based on Feature Selection Using Improved Linear Mixed Model

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Communications, Signal Processing, and Systems (CSPS 2019)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 571))

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Abstract

Artificial Intelligence (AI), which is designed to analyze huge amount of data, is introduced into wireless network analysis for assistance. Because the amount of data in this field is extremely massive, feature selection is a critical process. Compared to correlation based feature selection techniques, causal inference based Linear Mixed Models (LMM) can identify features with direct and fixed effects resulting from causal variables. However, Correlation based Feature Selection (CFS) does not give interpretable results and lacks justification. In this paper, an improved LMM is proposed for feature selection and used to analyze the performance of a wireless network. We introduce the \(L_2\) normalizer into the parameter estimation process of an LMM to regularize the standard model. Then, we utilize the results of the network analysis to construct a quality of users prediction model and use the improved LMM algorithm to select features and perform prediction. The experimental results prove that our proposed feature selection model outperforms other methods with respect to interpretability and prediction accuracy.

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Author information

Authors and Affiliations

  1. Key Laboratory of Universal Wireless Communication, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Chen Lu, Dong Liang & Dongxu Wang

  2. Research Institute of Forestry, Chinese Academy of Forestry, Beijing, 100091, China

    Yilin Zhao

Authors
  1. Chen Lu
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  2. Dong Liang
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  3. Dongxu Wang
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  4. Yilin Zhao
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Corresponding author

Correspondence to Dong Liang .

Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, University of Texas at Arlington, Arlington, TX, USA

    Qilian Liang

  2. College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China

    Wei Wang

  3. School of Information and Communication Engineering, Dalian University of Technology, Dalian, Liaoning, China

    Xin Liu

  4. School of Information Science and Technology, Dalian Maritime University, Dalian, Liaoning, China

    Zhenyu Na

  5. School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin, China

    Min Jia

  6. College of Physical and Electronic Information, Tianjin Normal University, Tianjin, China

    Baoju Zhang

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Lu, C., Liang, D., Wang, D., Zhao, Y. (2020). Network Service Analysis Based on Feature Selection Using Improved Linear Mixed Model. In: Liang, Q., Wang, W., Liu, X., Na, Z., Jia, M., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2019. Lecture Notes in Electrical Engineering, vol 571. Springer, Singapore. https://doi.org/10.1007/978-981-13-9409-6_315

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  • DOI: https://doi.org/10.1007/978-981-13-9409-6_315

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-9408-9

  • Online ISBN: 978-981-13-9409-6

  • eBook Packages: EngineeringEngineering (R0)

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