Proposition of a Theoretical Model for Missing Data Imputation using Deep Learning and Evolutionary Algorithms

TL;DR

This paper proposes a comprehensive theoretical model combining deep learning, evolutionary algorithms, and fuzzy logic to improve missing data imputation across various missing data mechanisms and patterns.

Contribution

It introduces a novel framework integrating autoencoders, genetic algorithms, swarm intelligence, and fuzzy logic for enhanced missing data imputation.

Findings

Deep neural networks with autoencoders outperform traditional methods.

Genetic algorithms and swarm intelligence improve imputation accuracy.

Fuzzy logic integration enhances handling of complex missing data scenarios.

Abstract

In the last couple of decades, there has been major advancements in the domain of missing data imputation. The techniques in the domain include amongst others: Expectation Maximization, Neural Networks with Evolutionary Algorithms or optimization techniques and K-Nearest Neighbor approaches to solve the problem. The presence of missing data entries in databases render the tasks of decision-making and data analysis nontrivial. As a result this area has attracted a lot of research interest with the aim being to yield accurate and time efficient and sensitive missing data imputation techniques especially when time sensitive applications are concerned like power plants and winding processes. In this article, considering arbitrary and monotone missing data patterns, we hypothesize that the use of deep neural networks built using autoencoders and denoising autoencoders in conjunction with genetic algorithms, swarm intelligence and maximum likelihood estimator methods as novel data imputation techniques will lead to better imputed values than existing techniques. Also considered are the missing at random, missing completely at random and missing not at random missing data mechanisms. We also intend to use fuzzy logic in tandem with deep neural networks to perform the missing data imputation tasks, as well as different building blocks for the deep neural networks like Stacked Restricted Boltzmann Machines and Deep Belief Networks to test our hypothesis. The motivation behind this article is the need for missing data imputation techniques that lead to better imputed values than existing methods with higher accuracies and lower errors.