Abstract
In this chapter, we examine the problem of missing data in high-dimensional datasets by taking into consideration the missing completely at random and missing at random mechanisms, as well as the arbitrary missing pattern. Additionally, this chapter employs a methodology based on deep learning and swarm intelligence algorithms in order to provide reliable estimates for missing data. The deep learning technique is used to extract features from the input data via an unsupervised learning approach by modeling the data distribution based on the input. 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. The proposed methodology in this chapter, therefore, has longer running times, however, the promising potential outcomes justify the trade-off. Also, basic knowledge of statistics is presumed.
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Leke, C.A., Marwala, T. (2019). Missing Data Estimation Using Firefly Algorithm. In: Deep Learning and Missing Data in Engineering Systems. Studies in Big Data, vol 48. Springer, Cham. https://doi.org/10.1007/978-3-030-01180-2_5
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DOI: https://doi.org/10.1007/978-3-030-01180-2_5
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