A Hybrid Wine Classification Model for Quality Prediction

“Wine is bottled poetry” a quote from Robert Louis Stevenson shows the wine is an exciting and complex product with distinctive qualities that make it different from other products. Therefore, the testing approach to determine the quality of the wine is complex and diverse. The opinion of a wine expert is influential, but it is also costly and subjective. Hence, many algorithms based on machine learning techniques have been proposed for predicting wine quality. However, most of them focus on analyzing different classifiers to figure out what the best classifier for wine quality prediction is. Instead of focusing on a particular classifier, it motivates us to find a more effective classifier. In this paper, a hybrid model that consists of two classifiers at least, e.g. the random forest, support vector machine, is proposed for wine quality prediction. To evaluate the performance of the proposed hybrid model, experiments also made on the wine datasets to show the merits of the hybrid model.