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Open Access

Classification on Grade, Price, and Region with Multi-Label and Multi-Target Methods in Wineinformatics

Department of Computer Science, University of Central Arkansas, Conway, AR 72034, USA.
Department of Computer Science, University of Alabama, Tuscaloosa, AL 35487, USA.
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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.

Keywords

classificationinformaticsmachine learningmulti-labelmulti-targetsupport vector machineswinewineinformatics

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Big Data Mining and Analytics
Volume 3 Issue 1,
March 2020
Pages 1-12
DOI: 10.26599/BDMA.2019.9020014
Cite this article:
Palmer J, Sheng VS, Atkison T, et al. Classification on Grade, Price, and Region with Multi-Label and Multi-Target Methods in Wineinformatics. Big Data Mining and Analytics, 2020, 3(1): 1-12. https://doi.org/10.26599/BDMA.2019.9020014

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Received: 02 July 2019
Accepted: 05 September 2019
Published: 19 December 2019
© The author(s) 2020

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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