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Feature subset selection for predicting the success of crowdfunding project campaigns

Author

Listed:
  • 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)

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.

Suggested Citation

  • Michael J. Ryoba & Shaojian Qu & Yongyi Zhou, 2021. "Feature subset selection for predicting the success of crowdfunding project campaigns," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(3), pages 671-684, September.
    • Handle: RePEc:spr:elmark:v:31:y:2021:i:3:d:10.1007_s12525-020-00398-4
    DOI: 10.1007/s12525-020-00398-4
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    References listed on IDEAS

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    2. Mi (Jamie) Zhou & Baozhou Lu & Weiguo (Patrick) Fan & G. Alan Wang, 2018. "Project description and crowdfunding success: an exploratory study," Information Systems Frontiers, Springer, vol. 20(2), pages 259-274, April.
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