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.
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References
Ahmad, F.S., Tyagi, D., Kaur, S. (2017). Predicting crowdfunding success with optimally weighted random forests, in: Infocom technologies and unmanned systems (trends and future directions)(ICTUS), 2017 international conference on. pp. 770–775.
Aljarah, I., Ala’M, A.-Z., Faris, H., Hassonah, M. A., Mirjalili, S., & Saadeh, H. (2018). Simultaneous feature selection and support vector machine optimization using the grasshopper optimization algorithm. Cognit. Comput., 1–18.
Aziz, R., Verma, C. K., & Srivastava, N. (2016). A fuzzy based feature selection from independent component subspace for machine learning classification of microarray data. Genomics data, 8, 4–15.
Belleflamme, P., Lambert, T., & Schwienbacher, A. (2014). Crowdfunding: Tapping the right crowd. Journal of Business Venturing, 29, 585–609.
Chandrashekar, G., & Sahin, F. (2014). A survey on feature selection methods. Computers and Electrical Engineering, 40, 16–28.
Chen, S.-Y., Chen, C.-N., Chen, Y.-R., Yang, C.-W., Lin, W.-C., Wei, C.-P. (2015) Will your project get the green light? Predicting the success of crowdfunding campaigns., in: PACIS. p. 79.
Chuang, L.-Y., Tsai, S.-W., & Yang, C.-H. (2011). Improved binary particle swarm optimization using catfish effect for feature selection. Expert Systems with Applications, 38, 12699–12707.
Crawford, B., Soto, R., Astorga, G., Garcia, J., Castro, C., & Paredes, F. (2017). Putting continuous metaheuristics to work in binary search spaces. Complexity, 2017.
Dash, M. (1997). Feature selection via set cover, in: Knowledge and Data Engineering Exchange Workshop, 1997. Proceedings. pp., 165–171.
Domeniconi, C., Peng, J., & Gunopulos, D. (2002). Locally adaptive metric nearest-neighbor classification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24, 1281–1285.
Elenchev, I., Vasilev, A., others (2017). Forecasting the success rate of reward based Crowdfunding projects. ZBW.
Emary, E., Zawbaa, H. M., & Hassanien, A. E. (2016a). Binary ant lion approaches for feature selection. Neurocomputing, 213, 54–65.
Emary, E., Zawbaa, H. M., & Hassanien, A. E. (2016b). Binary grey wolf optimization approaches for feature selection. Neurocomputing, 172, 371–381.
Etter, V., Grossglauser, M., Thiran, P. (2013). Launch hard or go home!: Predicting the success of kickstarter campaigns, in: Proceedings of the First ACM Conference on Online Social Networks. pp. 177–182.
Friedman, J., Hastie, T., & Tibshirani, R. (2001). The elements of statistical learning. Springer series in statistics new. NY, USA: York.
Gong, M., Yan, J., Shen, B., Ma, L., & Cai, Q. (2016). Influence maximization in social networks based on discrete particle swarm optimization. Inf. Sci. (Ny)., 367, 600–614.
Greenberg, M.D., Pardo, B., Hariharan, K., Gerber, E. (2013). Crowdfunding support tools: Predicting success & failure, in: CHI’13 Extended Abstracts on Human Factors in Computing Systems. pp. 1815–1820.
Guyon, I., & Elisseeff, A. (2003). An introduction to variable and feature selection. Journal of Machine Learning Research, 3, 1157–1182.
Harrington, P. (2012a). Machine learning in action. CT: Manning Greenwich.
Harrington, P. (2012b). Machine learning in action. Shelter Island: NY Manning Publ. Co..
Kaur, H., & Gera, J. (2017). Effect of social media connectivity on success of crowdfunding campaigns. Procedia Comput. Sci., 122, 767–774.
Kuppuswamy, V., Bayus, B.L. (2018). Crowdfunding creative ideas: The dynamics of project backers, in: The Economics of Crowdfunding. Springer, pp. 151–182.
Li, Y., Rakesh, V., Reddy, C.K. (2016). Project success prediction in crowdfunding environments, in: Proceedings of the Ninth ACM International Conference on Web Search and Data Mining. pp. 247–256.
Liu, H., Motoda, H. (1998). Feature extraction, construction and selection: A data mining perspective. Springer Science & Business Media.
Liu, H., & Motoda, H. (2007). Computational methods of feature selection. CRC Press.
Mafarja, M. M., & Mirjalili, S. (2017). Hybrid whale optimization algorithm with simulated annealing for feature selection. Neurocomputing, 260, 302–312.
McKinney, W., others, (2010). Data structures for statistical computing in python, in: Proceedings of the 9th Python in Science Conference. pp. 51–56.
Mirjalili, S., & Lewis, A. (2016). The whale optimization algorithm. Advances in Engineering Software, 95, 51–67.
Mollick, E. (2014). The dynamics of crowdfunding: An exploratory study. Journal of Business Venturing, 29, 1–16.
Osman, I. H., & Laporte, G. (1996). Metaheuristics: A bibliography. Springer.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., & Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12, 2825–2830.
Tahir, M. A., Bouridane, A., & Kurugollu, F. (2007). Simultaneous feature selection and feature weighting using hybrid Tabu search/K-nearest neighbor classifier. Pattern Recognition Letters, 28, 438–446.
Talbi, E.-G. (2002). A taxonomy of hybrid metaheuristics. Journal of Heuristics, 8, 541–564.
Talbi, E.-G. (2009). Metaheuristics: From design to implementation. John Wiley & Sons.
Van Der Walt, S., Colbert, S. C., & Varoquaux, G. (2011). The NumPy array: A structure for efficient numerical computation. Computing in Science & Engineering, 13, 22.
Wu, W. Q., Fu, M. X., & Zhao, L. M. (2016). Successful factors and herding phenomenon of crowdfunding. Soft Sci., 30, 5–8.
Zhou, M. J., Lu, B., Fan, W. P., & Wang, G. A. (2018). Project description and crowdfunding success: An exploratory study. Information Systems Frontiers, 20, 259–274.
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Ryoba, M.J., Qu, S. & Zhou, Y. Feature subset selection for predicting the success of crowdfunding project campaigns. Electron Markets 31, 671–684 (2021). https://doi.org/10.1007/s12525-020-00398-4
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DOI: https://doi.org/10.1007/s12525-020-00398-4