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Missing Data Estimation Using Ant-Lion Optimizer Algorithm

Part of the Studies in Big Data book series (SBD,volume 48)

Abstract

Ant-lion optimizer (ALO) algorithm is also a population-based meta-heuristic algorithm capable of finding approximate solutions to complex optimization problems. In this chapter, we present another new framework for missing data imputation in the high-dimensional dataset. A deep autoencoder is used in conjunction with the ALO algorithm (DL-ALO). The performance of the proposed technique is experimentally tested and compared against other existing methods of a similar nature using an off-line handwritten digits image recognition dataset. The results obtained are in line with those from previous chapters, further emphasizing the effectiveness and applicability of a deep learning framework in the domain being considered. Although the model portrays slightly longer execution times, which are a worthy trade-off when accuracy is of importance in real-world applications, it is important to further consider such frameworks.

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Correspondence to Collins Achepsah Leke .

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Leke, C.A., Marwala, T. (2019). Missing Data Estimation Using Ant-Lion Optimizer 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_7

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