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Shapley Feature Selection

Author

Listed:
  • Alex Gramegna

    (Department of Economics and Management, Neosurance and University of Pavia, 27100 Pavia, PV, Italy)

  • Paolo Giudici

    (Department of Economics and Management, Neosurance and University of Pavia, 27100 Pavia, PV, Italy)

Abstract

Feature selection is a popular topic. The main approaches to deal with it fall into the three main categories of filters, wrappers and embedded methods. Advancement in algorithms, though proving fruitful, may be not enough. We propose to integrate an explainable AI approach, based on Shapley values, to provide more accurate information for feature selection. We test our proposal in a real setting, which concerns the prediction of the probability of default of Small and Medium Enterprises. Our results show that the integrated approach may indeed prove fruitful to some feature selection methods, in particular more parsimonious ones like LASSO. In general the combination of approaches seems to provide useful information which feature selection algorithm can improve their performance with.

Suggested Citation

  • Alex Gramegna & Paolo Giudici, 2022. "Shapley Feature Selection," FinTech, MDPI, vol. 1(1), pages 1-9, February.
    • Handle: RePEc:gam:jfinte:v:1:y:2022:i:1:p:6-80:d:758125
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    References listed on IDEAS

    as
    1. Kursa, Miron B. & Rudnicki, Witold R., 2010. "Feature Selection with the Boruta Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 36(i11).
    2. Alex Gramegna & Paolo Giudici, 2020. "Why to Buy Insurance? An Explainable Artificial Intelligence Approach," Risks, MDPI, vol. 8(4), pages 1-9, December.
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