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Douzas, G., Bacao, F. and Last, F. (2018) Improving Imbalanced Learning through a Heuristic Oversampling Method Based on K-Means and Smote. Information Sciences, 465, 1-20.
https://doi.org/10.1016/j.ins.2018.06.056

has been cited by the following article:

• TITLE: Credit Score Classification Using Advanced Machine Learning: A Comprehensive Approach

  AUTHORS: Chaoya Yan, Xinyu Zhang, Jiaqing Shen

  KEYWORDS: Credit Scoring, Machine Learning, CatBoost, Feature Engineering, Class Imbalance, Financial Risk Assessment

  JOURNAL NAME: Journal of Software Engineering and Applications, Vol.18 No.3, March 27, 2025

  ABSTRACT: This paper presents a comprehensive machine learning approach for credit score classification, addressing key challenges in financial risk assessment. We propose an optimized CatBoost-based framework that integrates advanced feature engineering, systematic class imbalance handling, and robust evaluation metrics. Our methodology achieves strong classification performance, with AUC scores of 0.944, 0.858, and 0.928 for the Poor, Standard, and Good credit score classes, respectively. The system particularly excels in distinguishing high-risk (Poor) and low-risk (Good) credit profiles, while the Standard class remains the most challenging due to its overlapping characteristics. Through extensive experimentation and analysis, we provide valuable insights into feature importance and model behavior, offering practical implications for financial institutions and credit scoring systems.