Synapse
⌘+K
Synapse
PulseExploreClubsResearchersJournals
Instagram
HomeClubsExplore
August 1, 2025IAES International Journal of Artificial IntelligenceOpen Access

A survey of missing data imputation techniques: statistical methods, machine learning models, and GAN-based approaches

View Full Paper
Ask AI
Bookmark
Share

Authors

RSRifaa SadeghAMAhmed Ould Mohamed MoctarMSMohamed Lemine Salihi

Discussion

Loading...

Member takes

Overview

Analysis evaluates missing data imputation techniques, highlighting machine learning and GAN methods for diverse datasets.

Key Points

  • GAN-based methods outperform traditional statistical techniques in capturing non-linear relationships, ensuring better imputation accuracy.
  • Generative adversarial networks like GAIN and VIGAN are particularly effective for complex data types, including images and time series.
  • This analysis investigates various data missing mechanisms, including missing completely at random (MCAR) and missing at random (MAR).
  • Future research aims to optimize GAN structures and explore hybrid models to improve scalability and accuracy in practical applications.

Cite This Study

Sadegh et al. (2025) studied this question.

synapsesocial.com/papers/68af59e3ad7bf08b1eadeeeahttps://doi.org/10.11591/ijai.v14.i4.pp2876-2888
View Full Paper
Ask AI
Bookmark
Share

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Comparative analysis of imputation methods in machine learning models2025
  2. 2Advanced imputation techniques in failure prediction with optimized machine learning models using genetic algorithms2026
  3. 3Imputation of data Missing Not at Random: Artificial generation and benchmark analysis2024 · 24 citations
  4. 4Comprehensive Study of Data Imputation Techniques For Machine Learning Models2025
  5. 5Generative Neural Networks for Data Imputation in Longitudinal Epidemiological Studies2025