Abstract
In this chapter, we further amalgamate a deep learning framework and swarm intelligence for missing data estimation in high-dimensional datasets. Missing data is a recurrent issue in day-to-day datasets, resulting in a variety of setbacks which are often difficult for existing techniques which constitute narrow artificial intelligence architectures and computational intelligence methods. This is normally aligned with dimensionality and the number of rows. We propose a framework for the imputation procedure that uses a deep learning method with a swarm intelligence algorithm called deep learning-invasive weed optimization (DL-IWO) approach.
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Leke, C.A., Marwala, T. (2019). Missing Data Estimation Using Invasive Weed Optimization 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_8
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DOI: https://doi.org/10.1007/978-3-030-01180-2_8
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