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Random Mixed Field Model for Mixed-Attribute Data Restoration
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add RMF denoising before classifier | KNN · Statlog-Australian, tau=0.5 | classification accuracy: 0.5526 -> 0.6635 | Helped |
| add RMF denoising before classifier | SVM · Credit, tau=0.5 | classification accuracy: 0.4968 -> 0.6336 | Helped |
| add RMF denoising before classifier | KNN · Credit, tau=0.5 | classification accuracy: 0.5273 -> 0.6455 | Helped |
| add RMF denoising before classifier | SVM · Statlog-German, tau=0.5 | classification accuracy: 0.6580 -> 0.7096 | Helped |
| add RMF denoising before classifier | SVM · Adult, tau=0.5 | classification accuracy: 0.7817 -> 0.7920 | Helped |
| add RMF denoising before classifier | KNN · Adult, tau=0.1 | classification accuracy: 0.8174 -> 0.8174 | No clear effect |
| add RMF denoising before classifier | KNN · Statlog-German, tau=0.1 | classification accuracy: 0.7189 -> 0.7147 | Hurt |
| add RMF denoising before classifier | SVM · Adult, tau=0.1 | classification accuracy: 0.8356 -> 0.8413 | Helped |
| use RMF imputation instead of KNN imputation under noise | KNN classifier · Adult, rho=0.5 | classification accuracy: 0.3577 -> 0.7543 | Helped |
| use RMF imputation instead of regularized-EM imputation | KNN classifier · Adult, rho=0.5 | classification accuracy: 0.6929 -> 0.7543 | Helped |
| use RMF imputation instead of KNN imputation | SVM classifier · Adult, rho=0.5 | classification accuracy: 0.4947 -> 0.7547 | Helped |
| use RMF imputation instead of KNN imputation | KNN classifier · Statlog-German, rho=0.5 | classification accuracy: 0.5637 -> 0.6141 | Helped |
Closest papers by meaning
- DiffPuter: Empowering Diffusion Models for Missing Data Imputation (ICLR 2025 · similarity 0.84)
- Learning What Not to Impute: An Uncertainty-Aware Diffusion Framework for Meaningful Missingness (NeurIPS 2026 · similarity 0.84)
- RestoreGrad: Signal Restoration Using Conditional Denoising Diffusion Models with Jointly Learned Prior (ICML 2025 · similarity 0.84)
- Missing Data Imputation by Reducing Mutual Information with Rectified Flows (NeurIPS 2025 · similarity 0.84)
- Sampling Binary Data by Denoising through Score Functions (ICML 2025 · similarity 0.83)
- SeNM-VAE: Semi-Supervised Noise Modeling with Hierarchical Variational Autoencoder (CVPR 2024 · similarity 0.83)
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