RASF-LCS: Ranked Attribute Selection and Distance-Based Rule Filtering for Interpretable Credit Scoring
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.