Affiliations: [a] School of Information Management and Artificial Intelligence, Zhejiang University of Finance and Economics, Hangzhou, China
| [b] China Academy of Financial Research, Zhejiang University of Finance and Economics, Hangzhou, China
| [c] College of Business Administration, University of Texas at El Paso, El Paso, TX, United States
| [d] College of Computer Science and Technology, Zhejiang University, Hangzhou, China
Correspondence:
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Corresponding author. Wenyu Zhang, School of Information Management and Artificial Intelligence, Zhejiang University of Finance and Economics, Hangzhou, China. E-mail: [email protected].
Abstract: With the rapid development of commercial credit mechanisms, credit funds have become fundamental in promoting the development of manufacturing corporations. However, large-scale, imbalanced credit application information poses a challenge to accurate bankruptcy predictions. A novel multi-stage ensemble model with fuzzy clustering and optimized classifier composition is proposed herein by combining the fuzzy clustering-based classifier selection method, the random subspace (RS)-based classifier composition method, and the genetic algorithm (GA)-based classifier compositional optimization method to achieve accuracy in predicting bankruptcy among corporates. To overcome the inherent inflexibility of traditional hard clustering methods, a new fuzzy clustering-based classifier selection method is proposed based on the mini-batch k-means algorithm to obtain the best performing base classifiers for generating classifier compositions. The RS-based classifier composition method was applied to enhance the robustness of candidate classifier compositions by randomly selecting several subspaces in the original feature space. The GA-based classifier compositional optimization method was applied to optimize the parameters of the promising classifier composition through the iterative mechanism of the GA. Finally, six datasets collected from the real world were tested with four evaluation indicators to assess the performance of the proposed model. The experimental results showed that the proposed model outperformed the benchmark models with higher predictive accuracy and efficiency.
