Missing value imputation in Food Composition Data with Denoising Autoencoders
Journal
Journal of Food Composition and Analysis
Date Issued
2022-05-16
Author(s)
Gjorshoska, Ivana
Eftimov, Tome
Abstract
Missing data is a common problem in a wide range of fields that can arise as a result of different reasons: lack of
analysis, mishandling samples, measurement error, etc. The area of nutrition and food composition is no
exception to the problem of missing values. Missing data in food composition databases (FCDB) significantly
limits their usage. Commonly this problem is resolved by calculating mean or median from available data in the
same FCDB or borrowing values from other FCDBs, however, this method produces notable errors. This paper
focuses on missing value imputation using autoencoders, a deep learning algorithm that has the ability to
approximate values by learning a higher-level representation of its input. The data used was from the FCDBs
collected by the USDA FoodData Central. We compared the autoencoder imputation method with the commonly
used approaches fill-in-with-mean and fill-in-with-median, and the results show that the autoencoder method for
imputation provides superior results.
analysis, mishandling samples, measurement error, etc. The area of nutrition and food composition is no
exception to the problem of missing values. Missing data in food composition databases (FCDB) significantly
limits their usage. Commonly this problem is resolved by calculating mean or median from available data in the
same FCDB or borrowing values from other FCDBs, however, this method produces notable errors. This paper
focuses on missing value imputation using autoencoders, a deep learning algorithm that has the ability to
approximate values by learning a higher-level representation of its input. The data used was from the FCDBs
collected by the USDA FoodData Central. We compared the autoencoder imputation method with the commonly
used approaches fill-in-with-mean and fill-in-with-median, and the results show that the autoencoder method for
imputation provides superior results.
Subjects
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