Abstract
A new approach for credit scoring using principal component analysis (PCA) and support vector machine (SVM) in tandem is proposed in this paper. The proposed credit scoring hybrid algorithm consists of two basic steps. In the first step, PCA is employed for dimension reduction and in the second, SVM is employed for classification purpose, resulting in PCA-SVM hybrid model. The effectiveness of PCA-SVM model is evaluated using German and UK credit data sets. It is observed that PCA-SVM outperforms stand-alone SVM and PCA-Logistic Regression (LR) hybrid. However, in terms of sensitivity alone, LR outperformed PCA-SVM hybrid.
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Farquad, M.A.H., Ravi, V., Sriramjee, Praveen, G. (2011). Credit Scoring Using PCA-SVM Hybrid Model. In: Das, V.V., Stephen, J., Chaba, Y. (eds) Computer Networks and Information Technologies. CNC 2011. Communications in Computer and Information Science, vol 142. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19542-6_40
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DOI: https://doi.org/10.1007/978-3-642-19542-6_40
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