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Missing Data Estimation in High-Dimensional Datasets: A Swarm Intelligence-Deep Neural Network Approach
This deep learning technique is then used as part of the objective function for the swarm intelligence technique in order to estimate the missing data after a supervised fine-tuning phase by minimizing an error function based on the interrelationship and correlation between features in the dataset.
Proposition of a Theoretical Model for Missing Data Imputation using Deep Learning and Evolutionary Algorithms
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.
Missing Data Prediction and Classification: The Use of Auto-Associative Neural Networks and Optimization Algorithms
The methods suggested use the optimization algorithms to minimize an error function derived from training the auto-associative neural network during which the interrelationships between the inputs and the outputs are obtained and stored in the weights connecting the different layers of the network.
