A case-based reasoning-driven clustering imputation and noise-resistant classification learning paradigm for financial distress prediction with missing and noisy data

Key Points

• Prediction of financial distress shows improved accuracy with clustering and imputation techniques.
• Key metrics indicate a reduction in error rates when handling missing data and noise levels.
• Analysis employs a case-based reasoning strategy to enhance classification learning outcomes.
• This approach highlights the need for robust methods in financial contexts to address incomplete information.