Abstract
The development of credit risk assessment models is often considered within a classification context. Recent studies on the development of classification models have shown that a combination of methods often provides improved classification results compared to a single-method approach. Within this context, this study explores the combination of different classification methods in developing efficient models for credit risk assessment. A variety of methods are considered in the combination, including machine learning approaches and statistical techniques. The results illustrate that combined models can outperform individual models for credit risk analysis. The analysis also covers important issues such as the impact of using different parameters for the combined models, the effect of attribute selection, as well as the effects of combining strong or weak models.
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Doumpos, M., Zopounidis, C. Model combination for credit risk assessment: A stacked generalization approach. Ann Oper Res 151, 289–306 (2007). https://doi.org/10.1007/s10479-006-0120-x
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DOI: https://doi.org/10.1007/s10479-006-0120-x