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      Statistical and machine learning models in credit scoring: A systematic literature survey

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      Applied Soft Computing

      Elsevier BV

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          Support-vector networks

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            SMOTE: Synthetic Minority Over-sampling Technique

            An approach to the construction of classifiers from imbalanced datasets is described. A dataset is imbalanced if the classification categories are not approximately equally represented. Often real-world data sets are predominately composed of ``normal'' examples with only a small percentage of ``abnormal'' or ``interesting'' examples. It is also the case that the cost of misclassifying an abnormal (interesting) example as a normal example is often much higher than the cost of the reverse error. Under-sampling of the majority (normal) class has been proposed as a good means of increasing the sensitivity of a classifier to the minority class. This paper shows that a combination of our method of over-sampling the minority (abnormal) class and under-sampling the majority (normal) class can achieve better classifier performance (in ROC space) than only under-sampling the majority class. This paper also shows that a combination of our method of over-sampling the minority class and under-sampling the majority class can achieve better classifier performance (in ROC space) than varying the loss ratios in Ripper or class priors in Naive Bayes. Our method of over-sampling the minority class involves creating synthetic minority class examples. Experiments are performed using C4.5, Ripper and a Naive Bayes classifier. The method is evaluated using the area under the Receiver Operating Characteristic curve (AUC) and the ROC convex hull strategy.
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              machine.

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                Author and article information

                Contributors
                Journal
                Applied Soft Computing
                Applied Soft Computing
                Elsevier BV
                15684946
                June 2020
                June 2020
                : 91
                : 106263
                Article
                10.1016/j.asoc.2020.106263
                © 2020

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