Feature selection for Turkish Crowdfunding projects with using filtering and wrapping methods

Creative Commons License

Aydın C., Kılınç M.

ELECTRONIC COMMERCE RESEARCH AND APPLICATIONS, cilt.62, ss.1-14, 2023 (SCI-Expanded)

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 62
  • Basım Tarihi: 2023
  • Doi Numarası: 10.1016/j.elerap.2023.101340
  • Dergi Adı: ELECTRONIC COMMERCE RESEARCH AND APPLICATIONS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Social Sciences Citation Index (SSCI), Scopus, ABI/INFORM, Business Source Elite, Business Source Premier, Compendex, INSPEC
  • Sayfa Sayıları: ss.1-14
  • Dokuz Eylül Üniversitesi Adresli: Evet

Özet

Crowdfunding (CF) platforms host an increasing number of projects, where financial support from backers plays a vital role in project realization. Unfortunately, CF projects have experienced a downward trend in success rates. To address this issue, it is crucial to identify the factors that influence success by analyzing project characteristics. In our study, we collected project data from Turkey’s Fongogo CF platform, performed feature selection, and rigorously tested the results. We employed various methods such as Pearson correlation, mutual information statistics, chi-square, Fisher’s score from filtering methods, and recursive feature elimination from wrapper methods to understand feature relationships. We proposed a cross-validated recursive feature elimination method for feature selection. The identified success factors were classified using diverse machine learning algorithms, with the Gradient Boosting algorithm achieving the highest result of 84.28%. The results obtained with wrapper methods highlight the potential of utilizing features in decision support processes to enhance CF success.