A Partially Interpretable Adaptive Softmax Regression for Credit Scoring
| Autor: | Keun Ho Ryu, Oyun-Erdene Namsrai, Nipon Theera-Umpon, Lkhagvadorj Munkhdalai |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Process (engineering)
Computer science neural network 0211 other engineering and technologies 02 engineering and technology Machine learning computer.software_genre lcsh:Technology decision making lcsh:Chemistry 0202 electrical engineering electronic engineering information engineering General Materials Science Instrumentation lcsh:QH301-705.5 Fluid Flow and Transfer Processes 021103 operations research Artificial neural network business.industry lcsh:T Process Chemistry and Technology General Engineering Regression analysis softmax regression Regression lcsh:QC1-999 Computer Science Applications Core (game theory) lcsh:Biology (General) lcsh:QD1-999 Loan lcsh:TA1-2040 Softmax function Benchmark (computing) 020201 artificial intelligence & image processing Artificial intelligence business credit scoring application lcsh:Engineering (General). Civil engineering (General) computer lcsh:Physics |
| Zdroj: | Applied Sciences, Vol 11, Iss 3227, p 3227 (2021) Applied Sciences Volume 11 Issue 7 |
| ISSN: | 2076-3417 |
| Popis: | Credit scoring is a process of determining whether a borrower is successful or unsuccessful in repaying a loan using borrowers’ qualitative and quantitative characteristics. In recent years, machine learning algorithms have become widely studied in the development of credit scoring models. Although efficiently classifying good and bad borrowers is a core objective of the credit scoring model, there is still a need for the model that can explain the relationship between input and output. In this work, we propose a novel partially interpretable adaptive softmax (PIA-Soft) regression model to achieve both state-of-the-art predictive performance and marginally interpretation between input and output. We augment softmax regression by neural networks to make it adaptive for each borrower. Our PIA-Soft model consists of two main components: linear (softmax regression) and non-linear (neural network). The linear part explains the fundamental relationship between input and output variables. The non-linear part serves to improve the prediction performance by identifying the non-linear relationship between features for each borrower. The experimental result on public benchmark datasets shows that our proposed model not only outperformed the machine learning baselines but also showed the explanations that logically related to the real-world. |
| Databáze: | OpenAIRE |
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