Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Explainable AI and Voting Ensemble Model to Predict the Results of Seafood Product Importation Inspections

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.-S. 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.-S. 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

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.

Keywords

border inspection; decision tree; ensemble learning; explainable artificial intelligence; \begin{document}

Subject

Computer Science and Mathematics, Artificial Intelligence and Machine Learning

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