Research of Machine Learning and Feature Selection in Wine Quality Prediction

Hongjun Zhang

2024

Abstract

As a globally renowned beverage, the competitiveness of wine in the market significantly hinges on its quality. However, predicting wine quality proves to be a complex and intricate task due to its susceptibility to numerous influencing factors. In this context, the present research endeavors to employ contemporary machine learning methodologies to construct a dependable classification model aimed at accurately predicting wine quality. This study juxtaposes the efficacy of four models, namely Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Random Forest (RF), and Artificial Neural Network (ANN). Furthermore, it employs three feature selection techniques to exclude three features from the original eleven, thereby enhancing model performance. Findings reveal that across this study, SVM consistently outperforms other models, irrespective of the feature selection method employed. Additionally, it is noted that differing feature selection methods exert discernible impacts on model performance. Assisting vintners and consumers in accurately understanding and selecting high-quality wines, thereby fostering industry development and enhancing consumer experiences.

Paper Citation

in Harvard Style

Zhang H. (2024). Research of Machine Learning and Feature Selection in Wine Quality Prediction. In Proceedings of the 1st International Conference on Innovations in Applied Mathematics, Physics and Astronomy - Volume 1: IAMPA; ISBN 978-989-758-722-1, SciTePress, pages 5-12. DOI: 10.5220/0012982300004601

in Bibtex Style

@conference{iampa24,
author={Hongjun Zhang},
title={Research of Machine Learning and Feature Selection in Wine Quality Prediction},
booktitle={Proceedings of the 1st International Conference on Innovations in Applied Mathematics, Physics and Astronomy - Volume 1: IAMPA},
year={2024},
pages={5-12},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012982300004601},
isbn={978-989-758-722-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Innovations in Applied Mathematics, Physics and Astronomy - Volume 1: IAMPA
TI - Research of Machine Learning and Feature Selection in Wine Quality Prediction
SN - 978-989-758-722-1
AU - Zhang H.
PY - 2024
SP - 5
EP - 12
DO - 10.5220/0012982300004601
PB - SciTePress