Model combination for credit risk assessment: A stacked generalization approach |
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Authors: | Michael Doumpos Constantin Zopounidis |
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Institution: | (1) Technical University of Crete, Deptartment of Production Engineering and Management, Financial Engineering Laboratory, University Campus, 73100, Chania, Greece |
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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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Keywords: | Credit risk assessment Classification Model combination Stacked generalization |
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