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Feature subset selection for predicting the success of crowdfunding project campaigns

Michael J. Ryoba (), Shaojian Qu and Yongyi Zhou
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Michael J. Ryoba: University of Shanghai for Science and Technology
Shaojian Qu: University of Shanghai for Science and Technology
Yongyi Zhou: University of Shanghai for Science and Technology

Electronic Markets, 2021, vol. 31, issue 3, No 13, 684 pages

Abstract: Abstract Statistics from crowdfunding platforms show that a small percent of crowdfunding projects succeed in securing funds. This makes project creators eager to know the probability of success of their campaign and the features that contribute to its success before launching it on crowdfunding platforms. The existing literature focuses on examining success probability using the entire list of identified projects features. For situations for which project creators have limited resources to invest on the required project features, the list suggested by previous researchers is somewhat large and gives a small success probability. A minimal number of features that predict success with a higher probability can benefit project creators by providing them with insight and guidance in investing their limited resources. This paper presents a metaheuristic whale optimization algorithm (WOA) in the crowdfunding context to perform a complete search of a subset of features that have a high success contribution power. Experiments were conducted using WOA with the K-Nearest Neighbor (KNN) classifier on a Kickstarter dataset. Our approach obtains a subset of 9 features that predict the success of project campaigns with an accuracy (F-score) of 90.28% (90.11%), which is an increase (F-score) of 22.23% (21.61%) than when a complete set of features is used. The findings of this study contribute knowledge to various crowdfunding stakeholders, as they will provide new insights regarding a subset of essential features th4at influence the success of project campaigns with high accuracy.

Keywords: Crowdfunding; Crowdfunding prediction; Machine learning; Feature selection; Whale optimization algorithm (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s12525-020-00398-4

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