Classification on grade, price, and region with multi-label and multi-target methods in wineinformatics

James Palmer, Victor S. Sheng, Travis Atkison, Bernard Chen

Research output: Contribution to journalReview articlepeer-review

5 Scopus citations


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/multitarget 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 twoclass 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.

Original languageEnglish
Article number8935091
Pages (from-to)1-12
Number of pages12
JournalBig Data Mining and Analytics
Issue number1
StatePublished - Mar 2020


  • Classification
  • Informatics
  • Machine learning
  • Multi-label
  • Multi-target
  • Support vector machines
  • Wine
  • Wineinformatics


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