Evolutionary Ensemble Approach for Behavioral Credit Scoring
| Autor: | Alexander V. Boukhanovsky, Ivan Derevitskii, Klavdiya Bochenina, Alexander A. Kudryashov, Amir Uteuov, Anna V. Kalyuzhnaya, Nikolay O. Nikitin |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Structure (mathematical logic)
Ensemble forecasting Computer science business.industry media_common.quotation_subject Evolutionary algorithm 02 engineering and technology Machine learning computer.software_genre 01 natural sciences Set (abstract data type) 010104 statistics & probability 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Quality (business) Artificial intelligence 0101 mathematics business computer media_common |
| Zdroj: | Lecture Notes in Computer Science ISBN: 9783319937120 ICCS (3) |
| Popis: | This paper is concerned with the question of potential quality of scoring models that can be achieved using not only application form data but also behavioral data extracted from the transactional datasets. The several model types and a different configuration of the ensembles were analyzed in a set of experiments. Another aim of the research is to prove the effectiveness of evolutionary optimization of an ensemble structure and use it to increase the quality of default prediction. The example of obtained results is presented using models for borrowers default prediction trained on the set of features (purchase amount, location, merchant category) extracted from a transactional dataset of bank customers. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|