Red wine quality prediction through active learning

Abstract

General machine learning requires a large number of training samples, meaning a lot of manpower and material resources. To solve this problem, active learning and its algorithms are often used. Active learning is a form of semi-supervised machine learning where the algorithm can choose which data it wants to learn from and then use the smallest and most effective labeled data set to make predictions or classifications. This article will show an example of using active learning to predict red wine quality. The predictive modeling approach the author chose was the K-Nearest Neighbor and the active learning algorithm was ranked batch-mode sampling. By observing the learning curve, the author found that generally the prediction accuracy of active learning would increase as the number of iterations increased. The author compared the experiment with another case using classic iris flower data set, and concluded that the prediction accuracy of active learning for different data sets depends on many factors, such as the correlation between the independent and dependant variables, the size of the data set and the number of iterations.