Pregled bibliografske jedinice broj: 928067
Cost-Sensitive Learning from Imbalanced Datasets for Retail Credit Risk Assessment
Cost-Sensitive Learning from Imbalanced Datasets for Retail Credit Risk Assessment // TEM JOURNAL - Technology, Education, Management, Informatics, 7 (2018), 1; 59-73 doi:10.18421/TEM71-08 (međunarodna recenzija, članak, znanstveni)
CROSBI ID: 928067 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Cost-Sensitive Learning from Imbalanced Datasets for Retail Credit Risk Assessment
Autori
Oreški, Stjepan ; Oreški, Goran
Izvornik
TEM JOURNAL - Technology, Education, Management, Informatics (2217-8309) 7
(2018), 1;
59-73
Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni
Ključne riječi
genetic algorithm ; neural network ; credit risk assessment ; imbalanced datasets ; misclassification cost
Sažetak
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.
Izvorni jezik
Engleski
Znanstvena područja
Računarstvo, Informacijske i komunikacijske znanosti
Citiraj ovu publikaciju:
Časopis indeksira:
- Web of Science Core Collection (WoSCC)
- Emerging Sources Citation Index (ESCI)
