Research on Wine Classification Based on BP Neural Network

Abstract

The Wine Wine Dataset is a publicly available dataset from UCI above, which is the result of a chemical analysis of wines grown in the same region of Italy from three different varieties, red, white and rosé. BP neural network is a common artificial neural network model that is used to supervise learning tasks. It is a multi-layer feedforward neural network that updates network parameters through a backpropagation algorithm to minimize the loss function. Principal component analysis (PCA) is a commonly used data dimensionality reduction technique to discover the main features in a dataset. It maps the original data to a new coordinate system through a linear transformation, maximizing the variance of the data in the new coordinate system. This reduces the dimensions of the data while preserving as much information as possible. In this experiment, the wine dataset was used, combined with principal component analysis and BP neural network, to classify and reason three types of wines.