论文标题
使用一维卷积神经网络预测红酒质量
Prediction of Red Wine Quality Using One-dimensional Convolutional Neural Networks
论文作者
论文摘要
作为一种酒精饮料，葡萄酒一直普遍存在数千年，葡萄酒的质量评估在葡萄酒生产和贸易中非常重要。学者们提出了针对葡萄酒质量预测的各种深度学习和机器学习算法，例如支持向量机（SVM），随机森林（RF），K-Nearest邻居（KNN），深神经网络（DNN）和逻辑回归（LR）。但是，这些方法忽略了葡萄酒的物理和化学特性之间的内部关系，例如，pH值，固定酸度，柠檬酸等之间的相关性。为了填补空白，本文对这些属性进行了Pearson相关分析，PCA分析和Shapiro-Wilk测试，并结合了1D-CNN体系结构，以捕获相邻特征之间的相关性。此外，它实施了辍学和批处理技术，以提高提议模型的鲁棒性。大规模的实验表明，我们的方法可以超过葡萄酒质量预测的基线方法。此外，消融实验还证明了合并1-D CNN模块，辍学和归一化技术的有效性。
As an alcoholic beverage, wine has remained prevalent for thousands of years, and the quality assessment of wines has been significant in wine production and trade. Scholars have proposed various deep learning and machine learning algorithms for wine quality prediction, such as Support vector machine (SVM), Random Forest (RF), K-nearest neighbors (KNN), Deep neural network (DNN), and Logistic regression (LR). However, these methods ignore the inner relationship between the physical and chemical properties of the wine, for example, the correlations between pH values, fixed acidity, citric acid, and so on. To fill the gap, this paper conducts the Pearson correlation analysis, PCA analysis, and Shapiro-Wilk test on those properties and incorporates 1D-CNN architecture to capture the correlations among neighboring features. In addition, it implemented dropout and batch normalization techniques to improve the robustness of the proposed model. Massive experiments have shown that our method can outperform baseline approaches in wine quality prediction. Moreover, ablation experiments also demonstrate the effectiveness of incorporating the 1-D CNN module, Dropout, and normalization techniques.
