Jul 2026· GECCO Companion· pp. 245-248· 1 citation· 20 references
Computer Science
TL;DR
Experiments on loan approval datasets show that RASF improves accuracy by 3–5% over standard LCS while maintaining interpretable, rule-based outputs, highlighting the potential of RASF-LCS for explainable credit decision-making.
Abstract
Machine learning is widely used in credit scoring, but many high-performing models lack interpretability, limiting their use in regulated domains. Rule-based approaches such as Learning Classifier Systems (LCS) offer a balance between accuracy and explainability. This paper introduces Ranked Attribute Selection with Midpoint Filtering (RASF), an extension to LCS that enhances feature selection and rule validation. RASF combines mutual information-based feature ranking, guided attribute selection, and distance-based rule filtering. Experiments on loan approval datasets show that RASF improves accuracy by 3–5% over standard LCS while maintaining interpretable, rule-based outputs. These results highlight the potential of RASF-LCS for explainable credit decision-making.
RMAM integrates a new Modified Huffman Ranker (MHR) that computes an adaptive cut-off threshold to distinguish essential from redundant variables and demonstrates RMAM’s effectiveness as a practical variable selection approach for supervised learning applications.
This work compared five machine learning classifiers on a loan approval dataset: Random Forest, XGBoost, LightGBM, Logistic Regression, and Support Vector Machine and applied SHAP TreeExplainer to interpret the best-performing model.
Shengze Xu· Advances in Economics, Manag...· 0 citations
A Standardized Multi-Metric Evaluation Framework is developed to support the selection of more objective and reproducible machine learning models in the case of loan approval to identify models that have a balance of performance and efficiency in loan approval experiments.
Trihartono Agus, Agus Ilyas Ilyas, S. Sattriedi et al.· Jurnal Informatika: Jurnal P...· 0 citations
An explainable ensemble learning framework for predicting loan approval using Random Forest, XGBoost, and LightGBM models is proposed and it is revealed that CreditScore, EmploymentType, and Income are the most influential factors in determining loan approval decisions.
Mikaria Gultom· Journal of Digital Market an...· 0 citations
This study explores the use of Explainable Artificial intelligence techniques to improve the interpretability of credit default prediction and highlights the practical value of explainable machine learning in developing more understandable, trustworthy, and accountable credit risk assessment systems for real-world fina...
Muskan, B. Sidhu· International Journal of Com...· 0 citations
Results support the hypothesis that ensemble learning captures non-linear interactions between technical and information-quality dimensions, offering a reproducible, theoretically grounded foundation for data-driven MIS decision-making in digital commerce.
Mainul Islam Khan, Md Abutaher Dewan, K. Ahmed et al.· Information· 0 citations
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