@article{Palmer2020, 
author = {James Palmer and Victor S. Sheng and Travis Atkison and Bernard Chen},
title = {Classification on Grade, Price, and Region with Multi-Label and Multi-Target Methods in Wineinformatics},
year = {2020},
journal = {Big Data Mining and Analytics},
volume = {3},
number = {1},
pages = {1-12},
keywords = {classification, informatics, machine learning, multi-label, multi-target, support vector machines, wine, wineinformatics},
url = {https://www.sciopen.com/article/10.26599/BDMA.2019.9020014},
doi = {10.26599/BDMA.2019.9020014},
abstract = {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.}
}