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Introduction to Missing Data Estimation

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Deep Learning and Missing Data in Engineering Systems

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

Abstract

This chapter describes in detail the problem of missing data. It also describes the different missing data patterns and mechanisms. This is followed by a discussion of the classical missing data techniques ensued by a presentation of machine learning approaches to address the missing data problem. Subsequently, machine learning optimization techniques are presented for missing data estimation tasks.

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Authors and Affiliations

  1. Faculty of Engineering and Built Environment, University of Johannesburg, Auckland Park, South Africa

    Collins Achepsah Leke & Tshilidzi Marwala

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  1. Collins Achepsah Leke
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  2. Tshilidzi Marwala
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Correspondence to Collins Achepsah Leke .

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Leke, C.A., Marwala, T. (2019). Introduction to Missing Data Estimation. 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_1

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