In this paper, we examine the problem of missing data in high-dimensionaldatasets by taking into consideration the Missing Completely at Random andMissing at Random mechanisms, as well as theArbitrary missing pattern.Additionally, this paper employs a methodology based on Deep Learning and SwarmIntelligence algorithms in order to provide reliable estimates for missingdata. The deep learning technique is used to extract features from the inputdata via an unsupervised learning approach by modeling the data distributionbased on the input. This deep learning technique is then used as part of theobjective function for the swarm intelligence technique in order to estimatethe missing data after a supervised fine-tuning phase by minimizing an errorfunction based on the interrelationship and correlation between features in thedataset. The investigated methodology in this paper therefore has longerrunning times, however, the promising potential outcomes justify the trade-off.Also, basic knowledge of statistics is presumed.
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