Version 1
: Received: 27 November 2023 / Approved: 28 November 2023 / Online: 28 November 2023 (07:08:48 CET)
How to cite:
Khoeurn, S.; Lee, K.; Cho, W. Explainable AI and Voting Ensemble Model to Predict the
Results of Seafood Product Importation Inspections. Preprints2023, 2023111735. https://doi.org/10.20944/preprints202311.1735.v1
Khoeurn, S.; Lee, K.; Cho, W. Explainable AI and Voting Ensemble Model to Predict the
Results of Seafood Product Importation Inspections. Preprints 2023, 2023111735. https://doi.org/10.20944/preprints202311.1735.v1
Khoeurn, S.; Lee, K.; Cho, W. Explainable AI and Voting Ensemble Model to Predict the
Results of Seafood Product Importation Inspections. Preprints2023, 2023111735. https://doi.org/10.20944/preprints202311.1735.v1
APA Style
Khoeurn, S., Lee, K., & Cho, W. (2023). Explainable AI and Voting Ensemble Model to Predict the
Results of Seafood Product Importation Inspections. Preprints. https://doi.org/10.20944/preprints202311.1735.v1
Chicago/Turabian Style
Khoeurn, S., Kyunghee Lee and Wan-Sup Cho. 2023 "Explainable AI and Voting Ensemble Model to Predict the
Results of Seafood Product Importation Inspections" Preprints. https://doi.org/10.20944/preprints202311.1735.v1
Abstract
The lack of a generalizable machine learning model for predicting the safety of food for 1
human consumption is a significant challenge for policymakers and responsible authorities. This 2
study provides a step-by-step guide to predict the results of seafood product import inspections, 3
focusing on identifying and understanding the critical factors that influence these results. By compar- 4
ing the performances of an ensemble of machine learning models, this study combines the strengths 5
of multiple algorithms to improve the predictive accuracy and gain insights into the key factors 6
impacting them. The ensemble model based on the soft voting technique achieves superior perfor- 7
mance to that based on the hard voting technique in terms of the recall and area under the curve 8
(AUC) scores. The study discovered that various characteristics, such as the exporting country ratio, 9
major product category, overseas manufacturer ratio, importer ratio, and seasonal variation, had a 10
substantial influence on the models’ decisions. This research guide for predicting seafood product 11
import inspection results could pave the path for other items to follow.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
