Quality certification of wines of different qualities is important, and advances in quality certification require innovative models to further improve accuracy. This study incorporates a substantial dataset comprising 4898 samples and 12 physicochemical variables. Employing MIN-MAX normalization as a foundation, the primary focus is on utilizing the Support Vector Machine (SVM) optimized by Harris Hawks Optimization (HHO). HHO optimized the hyperparameters of the SVM to achieve a nearly thirty percent increase in model accuracy, in addition to this, comparative analysis reveals that HHO-SVM outperforms other models, including Decision Tree, AdaBoost, Backpropagation (BP) neural network, Naive Bayes, Logistic Regression, and the conventional SVM. Empirical findings from extensive experimentation on the WINE dataset demonstrate the achievement of a remarkable 100% accuracy by the model. The results shown that the HHO-SVM holds the potential to elevate wine production, thereby positively impacting the wine industry.

Xinyang Li. Research on Wine Quality Prediction with HHO-SVM. Academic Journal of Computing & Information Science (2024), Vol. 7, Issue 6: 70-75. https://doi.org/10.25236/AJCIS.2024.070611.

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