Conditional Diffusion Model for Missing Value Imputation
Rattachement africain : cn. Niveau de preuve : code pays fourni par la source.
Le résumé fourni par la source
With the rapid proliferation of social data, the prevalence of missing values has become increasingly common. Various factors, including human error and machine failure, contribute to the emergence of missing values in datasets. Datasets containing missing values not only consume storage space but also pose a significant obstacle to direct utilization, resulting in substantial resource wastage. Consequently, accurately imputing missing values has emerged as a focal point in research. Generative missing value imputation methods, leveraging generative models, have demonstrated notable efficacy in recent years by directly generating values for missing components based on observable data values. This paper introduces a novel generative method for missing value imputation based on a diffusion denoising model, termed the conditional diffusion model for missing value imputation (CDMVI). Specifically, CDMVI trains a conditional diffusion model using complete data samples (samples devoid of missing values) and subsequently utilizes the trained model to impute missing values in datasets. During the training stage (i.e., the forward process of the diffusion model), a subset of features is randomly selected from complete data samples, and varying levels of random noise are introduced as condition inputs to the noise predictor within the diffusion model. In the imputation stage (i.e., the backward process of the diffusion model), the missing segments of the data are initially replaced with random noise, serving as a guide for the diffusion model to generate complete samples. Experimental evaluations across multiple datasets demonstrate the competitive performance of our proposed CDMVI method.
Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.
Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Conditional Diffusion Model for Missing Value Imputation
- Date Crossref
- 29/01/2025
- Éditeur
- SAGE Publications
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
-
Nanning Normal University Guangxi Key Lab of Human–Machine Interaction and Intelligent Decision pays non établi dans la noticeUniversité ou école supérieure
-
Guangxi Science and Technology Department pays non établi dans la noticeOrganisme public
-
Guangxi Academy of Sciences pays non établi dans la noticeOrganisme public
-
Nanning Redcross Hospital pays non établi dans la noticeÉtablissement de santé
-
Ltd Guangxi Tourism Development Group Technology Co. pays non établi dans la noticeEntreprise
-
Guangxi Academic of Sciences pays non établi dans la noticeInstitution
Guangxi Key Lab of Human–Machine Interaction and Intelligent Decision — Nanning Normal University, Guangxi Science and Technology Department et Guangxi Academy of Sciences, avec 3 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.