DE_PSO_SVM: An Alternative Wine Classification Method Based on Machine Learning

Abstract

Accurate classification of wine quality may help to improve making technology of wine. For achieving more effective quality classification, a classification method named DE_PSO_SVM (dataset enhancement (DE)_particle swarm optimization (PSO)_support vector machine (SVM)) is proposed. The correlation between feature attributes and classification labels of wine samples were analyzed to achieve dimension reduction. DE was achieved by calculating the different weight sums of adjacent odd and even rows, both of which belong to the same class of samples. PSO was used to search for the optimal parameters of a Gaussian kernel function, which were substituted in the SVM model to classify wine. K-nearest-neighbor (KNN), random forest (RF) and classification and regression tree (CART) were also used to test the wine classification. In 7-fold cross-validation on three wine datasets, the average Precision, Recall, and F1score of DE_PSO_SVM were best. The results show that enhancing datasets with small samples and searching for the optimal super parameters by PSO improved the performance of the wine classification model.