Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-24599
Publication type: Book part
Type of review: Peer review (publication)
Title: Artificial neural networks to impute rounded zeros in compositional data
Authors: Templ, Matthias
et. al: No
DOI: 10.1007/978-3-030-71175-7_9
10.21256/zhaw-24599
Published in: Advances in Compositional Data Analysis : Festschrift in Honour of Vera Pawlowsky-Glahn
Editors of the parent work: Filzmoser, Peter
Hron, Karel
Martín-Fernández, Josep Antoni
Palarea-Albaladejo, Javier
Page(s): 163
Pages to: 187
Issue Date: 2021
Publisher / Ed. Institution: Springer
Publisher / Ed. Institution: Cham
ISBN: 978-3-030-71174-0
978-3-030-71175-7
Language: English
Subjects: Deep learning; Compositional data analsysis; Rounded zero; Imputation
Subject (DDC): 006: Special computer methods
Abstract: Methods of deep learning have become increasingly popular in recent years, but they have not arrived in compositional data analysis. Imputation methods for compositional data are typically applied on additive, centered, or isometric log-ratio representations of the data. Generally, methods for compositional data analysis can only be applied to observed positive entries in a data matrix. Therefore, one tries to impute missing values or measurements that were below a detection limit. In this paper, a new method for imputing rounded zeros based on artificial neural networks is shown and compared with conventional methods. We are also interested in the question whether for ANNs, a representation of the data in log-ratios for imputation purposes is relevant. It can be shown that ANNs are competitive or even performing better when imputing rounded zeros of data sets with moderate size. They deliver better results when data sets are big. Also, we can see that log-ratio transformations within the artificial neural network imputation procedure nevertheless help to improve the results. This proves that the theory of compositional data analysis and the fulfillment of all properties of compositional data analysis is still very important in the age of deep learning.
URI: https://digitalcollection.zhaw.ch/handle/11475/24599
Fulltext version: Accepted version
License (according to publishing contract): Licence according to publishing contract
Restricted until: 2022-06-02
Departement: School of Engineering
Organisational Unit: Institute of Data Analysis and Process Design (IDP)
Appears in collections:Publikationen School of Engineering

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