Abstract
In this study, support vector machine (SVM) is used as a metamodeling technique to design a business risk identification system. First of all, a bagging sampling technique is used to generate different training sets. Based on the different training sets, different SVM models with different parameters, i.e., base models, are then trained to formulate different classifiers. Finally, a SVM-based metamodel (i.e., metaclassifier) can be produced by learning from all base models. For illustration the proposed metamodel is applied to a real-world business insolvency risk classification problem.
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Lai, K.K., Yu, L., Huang, W., Wang, S. (2006). A Novel Support Vector Machine Metamodel for Business Risk Identification. In: Yang, Q., Webb, G. (eds) PRICAI 2006: Trends in Artificial Intelligence. PRICAI 2006. Lecture Notes in Computer Science(), vol 4099. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-36668-3_118
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DOI: https://doi.org/10.1007/978-3-540-36668-3_118
Publisher Name: Springer, Berlin, Heidelberg
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