A Generalized Wine Quality Prediction Framework by Evolutionary Algorithms.

Terry Hui Ye Chiu; Chienwen Wu; Chun Hao Chen

doi:10.9781/ijimai.2021.04.006

Authors

Terry Hui Ye Chiu National Taipei University of Technology
Chienwen Wu National Taipei University of Technology
Chun Hao Chen National Taipei University of Technology

DOI:

https://doi.org/10.9781/ijimai.2021.04.006

Keywords:

Decision Trees, Genetic Algorithms, Machine Learning, Random Forest, Support Vector Machine, Wine Quality Prediction

Supporting Agencies

This research was supported by the Ministry of Science and Technology of the Republic of China under grants MOST 108-2221-E032-037 and 109-2622-E-027-032.

Abstract

Wine is an exciting and complex product with distinctive qualities that makes it different from other manufactured products. Therefore, the testing approach to determine the quality of wine is complex and diverse. Several elements influence wine quality, but the views of experts can cause the most considerable influence on how people view the quality of wine. The views of experts on quality is very subjective, and may not match the taste of consumer. In addition, the experts may not always be available for the wine testing. To overcome this issue, many approaches based on machine learning techniques that get the attention of the wine industry have been proposed to solve it. However, they focused only on using a particular classifier with a specific set of wine dataset. In this paper, we thus firstly propose the generalized wine quality prediction framework to provide a mechanism for finding a useful hybrid model for wine quality prediction. Secondly, based on the framework, the generalized wine quality prediction algorithm using the genetic algorithms is proposed. It first encodes the classifiers as well as their hyperparameters into a chromosome. The fitness of a chromosome is then evaluated by the average accuracy of the employed classifiers. The genetic operations are performed to generate new offspring. The evolution process is continuing until reaching the stop criteria. As a result, the proposed approach can automatically find an appropriate hybrid set of classifiers and their hyperparameters for optimizing the prediction result and independent on the dataset. At last, experiments on the wine datasets were made to show the merits and effectiveness of the proposed approach.

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