Introduction
While the information age has made a large amount of data available for improved industrial process planning, occasional failures lead to missing data. The missing data may make it difficult to apply analytical models. Data imputation techniques help us fill the missing data with a reasonable prediction of what the missing values would have been.
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Lingras, P., Zhong, M., Sharma, S. (2008). Evolutionary Regression and Neural Imputations of Missing Values. In: Prasad, B. (eds) Soft Computing Applications in Industry. Studies in Fuzziness and Soft Computing, vol 226. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77465-5_8
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