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Autoencoder vs. Regression Neural Networks for Detecting Manipulated Wine Ratings

Authors:
Michaela Baumann
Michael Heinrich Baumann

Keywords: anomaly detection; manipulation identification; wine preferences; artificial neural networks; autoencoder

Abstract:
In this study, we analyze the ability of different (neural network based) detection methods to identify manipulated wine ratings for two "vinho verde" datasets. We find that autoencoders outperform regressions in terms of true/false positive rates. All in all, neural network based autoencoders seem to detect best, while classical linear models show the smallest performance variability. Most interestingly, linear model based autoencoders perform well within a reasonable computation time. Furthermore, hyperparameter tuning via sequential accumulative selection is established.

Pages: 7 to 13

Copyright: Copyright (c) IARIA, 2022

Publication date: May 22, 2022

Published in: conference

ISSN: 2308-4529

ISBN: 978-1-61208-972-0

Location: Venice, Italy

Dates: from May 22, 2022 to May 26, 2022