Fuzzy Graded Preprocessing for Robust Machine Learning: A Three-Stage Mamdani Framework with Interpretable Audit Trails

Data preprocessing methods for machine learning overwhelmingly rely on binary logic—a value is either valid or invalid—and the corrective action does not scale with error severity. This paper introduces GDEDC, a Mamdani-type fuzzy inference framework that replaces binary preprocessing with graded error detection and proportional correction. Operating in three stages—fuzzy anomaly scoring, nine-rule Mamdani FIS classification, and sigmoid-weighted imputation—the framework corrects each value in proportion to its estimated error severity while retaining 100% of observations and producing a human-readable audit trail. We evaluate GDEDC on five UCI datasets and the Pima Indians diabetes dataset with five classifiers across six noise levels (5–30%), comparing against five baselines including MICE. Under leakage-free conditions, deletion-based methods consistently underperform raw data, while correction-based methods (GDEDC, KNN Imputation, MICE) deliver significant improvements. GDEDC matches KNN Imputation and MICE at low noise and surpasses both at ≥20% noise: on noise-sensitive classifiers, GDEDC achieves the best Friedman rank at 20–30% noise. Real-world validation on the Pima dataset confirms generalizability, with GDEDC outperforming IQR by +2.97% (p < 0.001, d = 0.684). Ablation analysis shows that sigmoid-based proportional correction is the primary contributor (+2.02 pp), and the full pipeline outperforms every ablated variant at 10–20% noise.