02401nas a2200253   4500000000100000000000100001008004100002260001200043653001900055653002300074653002100097653001800118653002700136653002800163100002200191700001600213700001800229245007900247856007900326300001000405490000600415520171200421022001402133       2021                            d  c09/202110aDecision Trees10aGenetic Algorithms10aMachine Learning10aRandom Forest10aSupport Vector Machine10aWine Quality Prediction1 aTerry Hui-Ye Chiu1 aChienwen Wu1 aChun-Hao Chen00aA Generalized Wine Quality Prediction Framework by Evolutionary Algorithms  uhttps://www.ijimai.org/journal/sites/default/files/2021-08/ijimai6_7_6.pdf  a60-700 v63 aWine 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.  a1989-1660