Autoencoder, Principal Component Analysis and Support Vector Regression for Data Imputation

Abstract

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.

9 pages

References in corpus (3)

• A Tutorial on Principal Component Analysis
• Rough Sets Computations to Impute Missing Data
• Using Genetic Algorithms to Optimise Rough Set Partition Sizes for HIV Data Analysis

Cited by in corpus (4)

• Establishing strong imputation performance of a denoising autoencoder in a wide range of missing data problems
• Feature extraction using Latent Dirichlet Allocation and Neural Networks: A case study on movie synopses
• Missing Data using Decision Forest and Computational Intelligence
• DPER: Efficient Parameter Estimation for Randomly Missing Data