2007/09/16 by Vukosi N. Marivate, Vukosi Marivate, Fulufhelo V. Nelwamodo +4
Computer Science · #Artificial Intelligence (cs.AI) #Databases (cs.DB) #FOS: Computer and information sciences #Face and Expression Recognition #Neural Networks and Applications #Time Series Analysis and Forecasting #cs.AI #cs.DB
paper · pdf · doi:10.48550/arxiv.0709.2506
9 pages
arxiv created 2007/09/16 · openalex publication_date 2007/09/16 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Data collection often results in records that have missing values or variables. This investigation compares 3 different data imputation models and identifies their merits by using accuracy measures. Autoencoder Neural Networks, Principal components and Support Vector regression are used for prediction and combined with a genetic algorithm to then impute missing variables. The use of PCA improves the overall performance of the autoencoder network while the use of support vector regression shows promising potential for future investigation. Accuracies of up to 97.4 % on imputation of some of the variables were achieved.