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Input Relevance in Multi-Layer Perceptron for Fundraising

In: Mathematical and Statistical Methods for Actuarial Sciences and Finance

Author

Listed:
  • Diana Barro

    (Ca’ Foscari University of Venice, Department of Economics)

  • Luca Barzanti

    (University of Bologna, Department of Mathematics)

  • Marco Corazza

    (Ca’ Foscari University of Venice, Department of Economics)

  • Martina Nardon

    (Ca’ Foscari University of Venice, Department of Economics)

Abstract

In this contribution, we consider a Multi-Layer Perceptron (MLP) methodology for predicting specific gift features, particularly the count of donations and the gift amounts. Moreover, we use Garson’s indicator to evaluate the relative importance of the input variables to the output(s) in the MLP model with the aim of enhancing the effectiveness of fundraising campaigns. In the discussed application, the Donors’ behaviors are estimated using a simulated dataset that includes individual characteristics and information about donation history.

Suggested Citation

  • Diana Barro & Luca Barzanti & Marco Corazza & Martina Nardon, 2024. "Input Relevance in Multi-Layer Perceptron for Fundraising," Springer Books, in: Marco Corazza & Frédéric Gannon & Florence Legros & Claudio Pizzi & Vincent Touzé (ed.), Mathematical and Statistical Methods for Actuarial Sciences and Finance, pages 31-36, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-64273-9_6
    DOI: 10.1007/978-3-031-64273-9_6
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