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.
A. Tekín (Tue,) studied this question.