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Autoencoder vs. Regression Neural Networks for Detecting Manipulated Wine Ratings

Title data

Baumann, Michaela ; Baumann, Michael Heinrich:
Autoencoder vs. Regression Neural Networks for Detecting Manipulated Wine Ratings.
In: Mäkiö, Juho (ed.): ICCGI 2022, The Seventeenth International Multi-Conference on Computing in the Global Information Technology. - Venice, Italy : IARIA , 2022 . - pp. 7-13
ISBN 978-1-61208-972-0

Official URL: Volltext

Abstract in another language

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.

Further data

Item Type: Article in a book
Refereed: Yes
Keywords: anomaly detection; manipulation identification; wine preferences; artificial neural networks; autoencoder
Institutions of the University: Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Mathematics > Chair Mathematics V (Applied Mathematics)
Faculties > Faculty of Law, Business and Economics > Department of Economics > Chair Economics I - International Economics and Finance
Profile Fields > Advanced Fields > Nonlinear Dynamics
Research Institutions > Research Centres > Forschungszentrum für Modellbildung und Simulation (MODUS)
Result of work at the UBT: Yes
DDC Subjects: 000 Computer Science, information, general works > 004 Computer science
300 Social sciences > 330 Economics
500 Science > 510 Mathematics
Date Deposited: 09 Aug 2022 06:44
Last Modified: 09 Aug 2022 06:44
URI: https://eref.uni-bayreuth.de/id/eprint/71430