RASF-LCS: Ranked Attribute Selection and Distance-based Rule Filtering for Interpretable Credit Scoring
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Zahvie, Ahamed
Siddique, Abubakar
Nguyen, Trung
Iqbal, Muhammad
Browne, Will
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ACM
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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.
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Credit approval, learning classifier systems, interpretability, feature selection, mutual information, explain
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GECCO '26 Companion: Proceedings of the Genetic and Evolutionary Computation Conference Companion, San Jose, Costa Rica. Pages 245 - 248. ISBN 9798400724886
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Creative Commons Attribution 4.0 International
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Except where otherwise noted, this item's license is described as Creative Commons Attribution 4.0 International