Abstract
In this study, a multistage evolutionary programming (EP) based support vector machine (SVM) ensemble model is proposed for designing a corporate bankruptcy prediction system to discriminate healthful firms from bad ones. In the proposed model, a bagging sampling technique is first used to generate different training sets. Based on the different training sets, some different SVM models with different parameters are then trained to formulate different classifiers. Finally, these different SVM classifiers are aggregated into an ensemble output using an EP approach. For illustration, the proposed SVM ensemble model is applied to a real-world corporate failure prediction problem.
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Keywords
- Artificial Neural Network Model
- Ensemble Member
- Support Vector Machine Classifier
- Support Vector Machine Model
- Ensemble Classifier
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Yu, L., Lai, K.K., Wang, S. (2007). An Evolutionary Programming Based SVM Ensemble Model for Corporate Failure Prediction. In: Beliczynski, B., Dzielinski, A., Iwanowski, M., Ribeiro, B. (eds) Adaptive and Natural Computing Algorithms. ICANNGA 2007. Lecture Notes in Computer Science, vol 4432. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71629-7_30
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DOI: https://doi.org/10.1007/978-3-540-71629-7_30
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