Cost-Sensitive Learning from Imbalanced Datasets for Retail Credit Risk Assessment (CROSBI ID 249009)
Prilog u časopisu | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Oreški, Stjepan ; Oreški, Goran
engleski
Cost-Sensitive Learning from Imbalanced Datasets for Retail Credit Risk Assessment
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.
genetic algorithm ; neural network ; credit risk assessment ; imbalanced datasets ; misclassification cost
nije evidentirano
nije evidentirano
nije evidentirano
nije evidentirano
nije evidentirano
nije evidentirano
Podaci o izdanju
Trošak objave rada u otvorenom pristupu
Povezanost rada
Računarstvo, Informacijske i komunikacijske znanosti