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Comparison of Machine Learning Methods for Life Trajectory Analysis in Demography

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Abstract

Nowadays there are representative volumes of demographic data which are the sources for extraction of demographic sequences that can be further analysed and interpreted by domain experts. Since traditional statistical methods cannot face the emerging needs of demography, we used modern methods of pattern mining and machine learning to achieve better results. In particular, our collaborators, the demographers, are interested in two main problems: prediction of the next event in a personal life trajectory and finding interesting patterns in terms of demographic events for the gender feature. The main goal of this paper is to compare different methods by accuracy for these tasks. We have considered interpretable methods such as decision trees and semi- and non-interpretable methods, such as the SVM method with custom kernels and neural networks. The best accuracy results are obtained with a two-channel convolutional neural network. All the acquired results and the found patterns are passed to the demographers for further investigation.

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Notes

  1. 1.

    https://keras.io.

  2. 2.

    https://github.com/anya-m/2CNNSeqDem/.

  3. 3.

    For emerging patterns in classification setting cf. [8].

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Acknowledgment

The authors would like to thank Prof. G. Dong for his interest in our previous work on prefix-based emerging sequential patterns.

The study was implemented in the framework of the Basic Research Program at the National Research University Higher School of Economics and funded by the Russian Academic Excellence Project ‘5-100’. This research is also supported by the Faculty of Social Sciences, National Research University Higher School of Economics.

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Authors and Affiliations

  1. National Research University Higher School of Economics, Moscow, Russia

    Anna Muratova, Ekaterina Mitrofanova & Robiul Islam

  2. Innopolis University, Innopolis, Russia

    Robiul Islam

Authors
  1. Anna Muratova
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  2. Ekaterina Mitrofanova
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  3. Robiul Islam
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Corresponding author

Correspondence to Anna Muratova .

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Editors and Affiliations

  1. Wrocław University of Science and Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

  2. King Mongkut's Institute of Technology Ladkrabang, Bangkok, Thailand

    Suphamit Chittayasothorn

  3. Nanyang Technological University, Singapore, Singapore

    Dusit Niyato

  4. Wrocław University of Science and Technology, Wrocław, Poland

    Bogdan Trawiński

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Muratova, A., Mitrofanova, E., Islam, R. (2021). Comparison of Machine Learning Methods for Life Trajectory Analysis in Demography. In: Nguyen, N.T., Chittayasothorn, S., Niyato, D., Trawiński, B. (eds) Intelligent Information and Database Systems. ACIIDS 2021. Lecture Notes in Computer Science(), vol 12672. Springer, Cham. https://doi.org/10.1007/978-3-030-73280-6_50

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  • DOI: https://doi.org/10.1007/978-3-030-73280-6_50

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