Prediction of Wine Quality Using Ensemble Learning Approach of Machine Learning

DOI

10.2991/978-94-6463-042-8_110How to use a DOI?

Keywords

wine quality; data analysis; machine learning; ensemble learning

Abstract

The improvement of consumption level leads to increase in demand for red wine.What follows with is the contradiction between production speed and quality control. Predicting red wine quality in traditional process demands a lot of time and labor costs makes the whole productive process more expensive. Nowadays, benefit from Machine Learning (ML), especially the rise of ensemble learning, red wine quality prediction could have a more efficient and more convenient way. During this process, use a certain amount of data of several specific features to be trained by ensemble learning model to find the best result could be used in prediction of the red wine quality. The best model combination we found is stacking ensemble learning with accuracy rate of 0.87. This research could be a significant reference for red wine test or further use in the related industrial manufacture to reduce the cost of quality production.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

TY  - CONF
AU  - Qingwen Zeng
PY  - 2022
DA  - 2022/12/29
TI  - Prediction of Wine Quality Using Ensemble Learning Approach of Machine Learning
BT  - Proceedings of the 2022 International Conference on mathematical statistics and economic analysis (MSEA 2022)
PB  - Atlantis Press
SP  - 770
EP  - 774
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-042-8_110
DO  - 10.2991/978-94-6463-042-8_110
ID  - Zeng2022
ER  -