Grunnleggende konsepter
DiffImpute is a novel Denoising Diffusion Probabilistic Model (DDPM) tailored for tabular data imputation, outperforming traditional imputation techniques.
Sammendrag
Traditional imputation methods often yield suboptimal results and impose computational burdens.
DiffImpute is trained on complete tabular datasets to produce credible imputations for missing entries.
Four tabular denoising networks are utilized in DDPM: MLP, ResNet, Transformer, and U-Net.
Harmonization enhances coherence between observed and imputed data.
Empirical evaluations on diverse datasets show the superiority of DiffImpute, especially when paired with the Transformer denoising network.
Statistikk
DiffImpute consistently outperforms competitors with an average ranking of 1.7 and minimal standard deviation.
The code for DiffImpute is available at https://github.com/Dendiiiii/DiffImpute.