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


 
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1. Title Title of document Application of predictive artificial intelligence (AI) models to estimate the success of crowdfunding: Metaheuristic feature selection
 
2. Creator Author's name, affiliation, country Zoltán Zéman; Doctoral School of Management and Business Administration, John von Neumann University (NJE GSZDI//JNU-DSMBA); Hungary
 
2. Creator Author's name, affiliation, country Botond Géza Kálmán; Department of Economics and Management, Faculty of Economics, Kodolányi University of Applied Sciences (KJE GTK GMT//KU FE DEM); Institute of Economic Research, Faculty of Economics, Kodolányi University of Applied Sciences (KJE GTK GMT//KU FE DEM); Department of Finance and Accounting, Faculty of Economics, John von Neumann University (NJE GTK PSZT//JNU FE DFA); Institute of Economics and Finance, Budapest Metropolitan University of Applied Sciences (METU ÜKT GMT//KU FE DEM); Hungary
 
2. Creator Author's name, affiliation, country Szilárd Malatyinszki; Department of Economics and Management, Faculty of Economics, Kodolányi University of Applied Sciences (KJE GTK GMT//KU FE DEM); Hungary
 
3. Subject Discipline(s)
 
3. Subject Keyword(s) crowdfunding; feature selection optimization; self-enhanced chimp optimization algorithm; convolutional neural network; Kickstarter; Indiegogo
 
4. Description 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.
 
5. Publisher Organizing agency, location EnPress Publisher
 
6. Contributor Sponsor(s)
 
7. Date (YYYY-MM-DD) 2024-12-24
 
8. Type Status & genre Peer-reviewed Article
 
8. Type Type
 
9. Format File format PDF
 
10. Identifier Uniform Resource Identifier https://systems.enpress-publisher.com/index.php/jipd/article/view/7934
 
10. Identifier Digital Object Identifier (DOI) https://doi.org/10.24294/jipd7934
 
11. Source Title; vol., no. (year) Journal of Infrastructure, Policy and Development; Vol 8, No 16 (Published)
 
12. Language English=en en
 
14. Coverage Geo-spatial location, chronological period, research sample (gender, age, etc.)
 
15. Rights Copyright and permissions Copyright (c) 2024 Zoltán Zéman, Botond Géza Kálmán, Szilárd Malatyinszki
https://creativecommons.org/licenses/by/4.0/