Client evaluation decision models in the credit scoring tasks
| Autor: | Paweł Ziemba, Jarosław Becker, Aleksandra Radomska-Zalas |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Computer science
business.industry media_common.quotation_subject 020206 networking & telecommunications Feature selection 02 engineering and technology Machine learning computer.software_genre Random forest Client evaluation Cash 0202 electrical engineering electronic engineering information engineering General Earth and Planetary Sciences 020201 artificial intelligence & image processing Artificial intelligence business Classifier (UML) Decision model computer General Environmental Science media_common Credit risk |
| Zdroj: | KES |
| ISSN: | 1877-0509 |
| DOI: | 10.1016/j.procs.2020.09.068 |
| Popis: | One of the important decision-making problems of modern financial institutions is credit scoring, which involves assessing credit risk. Decision-making models based on classifiers and feature selection methods that reduce the complexity of a decision problem by limiting the number of conditional attributes find use in such problems. The article examines the effectiveness of various combinations of classifiers and feature selection methods in the problem of credit risk assessment. The results of the conducted research indicate that for the considered set of data on cash loans granted, the Correlation-based Feature Selection method is the best method among the considered ones, and the Random Forest is the most effective classifier. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|