Input Relevance in Multi-Layer Perceptron for Fundraising

Diana Barro (), Luca Barzanti (), Marco Corazza () and Martina Nardon ()
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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

A chapter in Mathematical and Statistical Methods for Actuarial Sciences and Finance, 2024, pp 31-36 from Springer

Abstract: 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.

Keywords: Multi-Layer Perceptron; Input relevance; Garson’s indicator; Fundraising Management (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-64273-9_6

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DOI: 10.1007/978-3-031-64273-9_6

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