Abstract
With the huge advancement of financial institution, credit risk prediction assumes a critical part to grant a loan to the customer and helps the financial institution to minimize their misfortunes. Despite the fact that there are different statistical and artificial intelligent methods available, there is no single best strategy for credit risk prediction. In our work, we have used feature selection and feature extraction methods as preprocessing techniques before building a classifier model. To validate the feasibility and effectiveness of our models, three credit data sets are picked namely Australia, German, and Japanese. Experimental results demonstrates that the SVM classifier performs better among several classifier methods, i.e., NB, LogR, DT, and KNN with LDA feature extraction technique. Test result demonstrates that the feature extraction preprocessing technique with base classifiers are the best suited for credit risk prediction.
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Sivasankar, E., Selvi, C., Mala, C. (2017). A Study of Dimensionality Reduction Techniques with Machine Learning Methods for Credit Risk Prediction. In: Behera, H., Mohapatra, D. (eds) Computational Intelligence in Data Mining. Advances in Intelligent Systems and Computing, vol 556. Springer, Singapore. https://doi.org/10.1007/978-981-10-3874-7_7
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DOI: https://doi.org/10.1007/978-981-10-3874-7_7
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