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Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

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A multi-level classification based ensemble and feature extractor for credit risk assessment.

Yuanyuan Wang¹, Zhuang Wu¹, Jing Gao¹

¹School of Management and Engineering, Capital University of Economics and Business, BeiJing, Fengtai District, Beijing, China.

Peerj. Computer Science

|March 4, 2024

Summary

This study introduces a new method for classifying personal credit risk, improving loan decision-making. The Multi-Level Classification based Ensemble and Feature Extractor (MLCEFE) enhances accuracy in multi-class credit risk assessment.

Keywords:

Ensemble learning Multi-level classification Personal credit risk SMOTE + Tomek links sampling

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Area of Science:

Machine Learning
Financial Risk Management
Data Science

Background:

Increasing demand for loans necessitates improved customer credit risk assessment by financial institutions.
Accurate credit risk classification is crucial for informed loan decisions, optimal allocation, and pre-loan risk reduction.

Purpose of the Study:

To propose and evaluate a novel Multi-Level Classification based Ensemble and Feature Extractor (MLCEFE) for personal credit risk multi-classification.
To enhance the accuracy and effectiveness of credit risk assessment models.

Main Methods:

Utilized SMOTE + Tomek links to address data imbalance issues.
Employed deep neural network (DNN), auto-encoder (AE), and principal component analysis (PCA) for abstract feature extraction.
Integrated multiple ensemble learners for improved multi-classification performance.

Main Results:

The MLCEFE model demonstrated superior performance in personal credit risk multi-classification.
Achieved remarkable results compared to existing classification methods.

Conclusions:

The proposed MLCEFE method effectively improves personal credit risk multi-classification.
MLCEFE offers a robust approach for financial institutions to better manage credit risk.