Application of predictive artificial intelligence (AI) models to estimate the success of crowdfunding: Metaheuristic feature selection

Zoltán Zéman, Botond Géza Kálmán, Szilárd Malatyinszki

Article ID: 7934
Vol 8, Issue 16, 2024

(Abstract)

Abstract


This research presents a novel approach utilizing a self-enhanced chimp optimization algorithm (COA) for feature selection in crowdfunding success prediction models, which offers significant improvements over existing methods. By focusing on reducing feature redundancy and improving prediction accuracy, this study introduces an innovative technique that enhances the efficiency of machine learning models used in crowdfunding. The results from this study could have a meaningful impact on how crowdfunding campaigns are designed and evaluated, offering new strategies for creators and investors to increase the likelihood of campaign success in a rapidly evolving digital funding landscape.


Keywords


crowdfunding; feature selection optimization; self-enhanced chimp optimization algorithm; convolutional neural network; Kickstarter; Indiegogo

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DOI: https://doi.org/10.24294/jipd7934

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