Input Relevance in Multi-Layer Perceptron for Fundraising

被引：0

作者：

Barro, Diana ^{[1
]}

Barzanti, Luca ^{[2
]}

Corazza, Marco ^{[1
]}

Nardon, Martina ^{[1
]}

机构：

[1] Ca Foscari Univ Venice, Dept Econ, Cannaregio 873, I-30121 Venice, Italy

[2] Univ Bologna, Dept Math, Piazza Porta San Donato 5, I-40126 Bologna, Italy

来源：

MATHEMATICAL AND STATISTICAL METHODS FOR ACTUARIAL SCIENCES AND FINANCE, MAF2024 | 2024年

关键词：

Multi-Layer Perceptron; Input relevance; Garson's indicator; Fundraising Management;

D O I：

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

中图分类号：

F8 [财政、金融];

学科分类号：

0202 ;

摘要：

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.

引用

页码：31 / 36

页数：6

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