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Searching for Interpretable Demographic Patterns

Author

Listed:
  • Muratova, Anna
  • Islam, Robiul
  • Mitrofanova, Ekaterina S.
  • Ignatov, Dmitry I.
Abstract
Nowadays there is a large amount of demographic data which should be analyzed and interpreted. From accumulated demographic data, more useful information can be extracted by applying modern methods of data mining. Two kinds of experiments are considered in this work: 1) generation of additional secondary features from events and evaluation of its influence on accuracy; 2) exploration of features influence on classification result using SHAP (SHapley Additive exPlanations). An algorithm for creating secondary features is proposed and applied to the dataset. The classifications were made by two methods, SVM and neural networks, and the results were evaluated. The impact of events and features on the classification results was evaluated using SHAP; it was demonstrated how to tune model for improving accuracy based on the obtained values. Applying convolutional neural network for sequences of events allowed improve classification accuracy and surpass the previous best result on the studied demographic dataset.

Suggested Citation

  • Muratova, Anna & Islam, Robiul & Mitrofanova, Ekaterina S. & Ignatov, Dmitry I., 2019. "Searching for Interpretable Demographic Patterns," MPRA Paper 97305, University Library of Munich, Germany, revised 23 Sep 2019.
    • Handle: RePEc:pra:mprapa:97305
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    File URL: https://mpra.ub.uni-muenchen.de/97305/1/paper2.pdf
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    References listed on IDEAS

    as
    1. Muratova, Anna & Sushko, Pavel & Espy, Thomas H., 2017. "Black-Box Classification Techniques for Demographic Sequences : from Customised SVM to RNN," MPRA Paper 82799, University Library of Munich, Germany.
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    Cited by:

    1. Robiul Islam & Andrey V. Andreev & Natalia N. Shusharina & Alexander E. Hramov, 2022. "Explainable Machine Learning Methods for Classification of Brain States during Visual Perception," Mathematics, MDPI, vol. 10(15), pages 1-25, August.

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      More about this item

      Keywords

      data mining; demographics; neural networks; classification; SHAP; interpretation;
      All these keywords.

      JEL classification:

      • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics
      • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
      • I00 - Health, Education, and Welfare - - General - - - General
      • J13 - Labor and Demographic Economics - - Demographic Economics - - - Fertility; Family Planning; Child Care; Children; Youth

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