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Multilayer Perceptron Optimization on Imbalanced Data Using SVM-SMOTE and One-Hot Encoding for Credit Card Default Prediction
Journal of Advances in Information Systems and Technology
Credit risk assessment analysis by classifying potential users is an important process to reduce the occurrence of default users. The problems faced from the classification process using real-world datasets are imbalanced data that causes bias-to-majority in model training outcomes. These problems cause the algorithm to only focus on the majority class and ignore the minority class, even though both classes have the same important role. To overcome this problem, a combination of One-hotdoi:10.15294/jaist.v3i2.57061 fatcat:cwkqsutklbahrfsriimu7grwni