classification, informatics, machine learning, multi-label, multi-target, support vector machines, wine, wineinformatics
Classifying wine according to their grade, price, and region of origin is a multi-label and multi-target problem in wineinformatics. Using wine reviews as the attributes, we compare several different multi-label/multi-target methods to the single-label method where each label is treated independently. We explore both single-label and multi-label approaches for a two-class problem for each of the labels and we explore both single-label and multi-target approaches for a four-class problem on two of the three labels, with the third label remaining a two-class problem. In terms of per-label accuracy, the single-label method has the best performance, although some multi-label methods approach the performance of single-label. However, multi-label/multi-target metrics approaches do exceed the performance of the single-label method.
Tsinghua University Press
James Palmer, Victor S. Sheng, Travis Atkison et al. Classification on Grade, Price, and Region with Multi-Label and Multi-Target Methods in Wineinformatics. Big Data Mining and Anyalytics 2020, 03(01): 1-12.