Cost-Sensitive Learning from Imbalanced Datasets for Retail Credit Risk Assessment
Cost-Sensitive Learning from Imbalanced Datasets for Retail Credit Risk Assessment

Summary/Abstract: In the present study we propose a new classification technique based on genetic algorithm and neural network, optimized for the cost-sensitive measure and applied to retail credit risk assessment. The relative cost of misclassification, which properly accounts for different misclassification costs of minority and majority classes, is used as the primary evaluation measure. The test of the new algorithm is performed on Croatian and German retail credit datasets for seven different cost ratios. An empirical comparison with others in the literature presented models demonstrates the potential of the new technique in terms of misclassification costs.

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