Abstract
In this study, we use least square support vector machines (LSSVM) to construct a credit scoring model and introduce conjoint analysis technique to analyze the relative importance of each input feature for making the decision in the model. A test based on a real-world credit dataset shows that the proposed model has good classification accuracy and can help explain the decision. Hence, it is an alternative model for credit scoring tasks.
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Lai, K.K., Zhou, L., Yu, L. (2007). A Two-Phase Model Based on SVM and Conjoint Analysis for Credit Scoring. In: Shi, Y., van Albada, G.D., Dongarra, J., Sloot, P.M.A. (eds) Computational Science – ICCS 2007. ICCS 2007. Lecture Notes in Computer Science, vol 4488. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72586-2_72
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DOI: https://doi.org/10.1007/978-3-540-72586-2_72
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72585-5
Online ISBN: 978-3-540-72586-2
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