Classification on Grade, Price, and Region with Multi-Label and Multi-Target Methods in 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 independentl...
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| Format: | Article |
| Language: | English |
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Tsinghua University Press
2020-03-01
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| Series: | Big Data Mining and Analytics |
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| Online Access: | https://www.sciopen.com/article/10.26599/BDMA.2019.9020014 |
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| author | James Palmer Victor S. Sheng Travis Atkison Bernard Chen |
| author_facet | James Palmer Victor S. Sheng Travis Atkison Bernard Chen |
| author_sort | James Palmer |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-f968fad60726478fbfb861505c7a96d8 |
| institution | Kabale University |
| issn | 2096-0654 |
| language | English |
| publishDate | 2020-03-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Big Data Mining and Analytics |
| spelling | doaj-art-f968fad60726478fbfb861505c7a96d82025-02-02T06:50:33ZengTsinghua University PressBig Data Mining and Analytics2096-06542020-03-013111210.26599/BDMA.2019.9020014Classification on Grade, Price, and Region with Multi-Label and Multi-Target Methods in WineinformaticsJames Palmer0Victor S. Sheng1Travis Atkison2Bernard Chen3<institution content-type="dept">Department of Computer Science</institution>, <institution>University of Central Arkansas</institution>, <city>Conway</city>, <state>AR</state> <postal-code>72034</postal-code>, <country>USA</country>.<institution content-type="dept">Department of Computer Science</institution>, <institution>University of Central Arkansas</institution>, <city>Conway</city>, <state>AR</state> <postal-code>72034</postal-code>, <country>USA</country>.<institution content-type="dept">Department of Computer Science</institution>, <institution>University of Alabama</institution>, <city>Tuscaloosa</city>, <state>AL</state> <postal-code>35487</postal-code>, <country>USA</country>.<institution content-type="dept">Department of Computer Science</institution>, <institution>University of Central Arkansas</institution>, <city>Conway</city>, <state>AR</state> <postal-code>72034</postal-code>, <country>USA</country>.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.https://www.sciopen.com/article/10.26599/BDMA.2019.9020014classificationinformaticsmachine learningmulti-labelmulti-targetsupport vector machineswinewineinformatics |
| spellingShingle | James Palmer Victor S. Sheng Travis Atkison Bernard Chen Classification on Grade, Price, and Region with Multi-Label and Multi-Target Methods in Wineinformatics Big Data Mining and Analytics classification informatics machine learning multi-label multi-target support vector machines wine wineinformatics |
| title | Classification on Grade, Price, and Region with Multi-Label and Multi-Target Methods in Wineinformatics |
| title_full | Classification on Grade, Price, and Region with Multi-Label and Multi-Target Methods in Wineinformatics |
| title_fullStr | Classification on Grade, Price, and Region with Multi-Label and Multi-Target Methods in Wineinformatics |
| title_full_unstemmed | Classification on Grade, Price, and Region with Multi-Label and Multi-Target Methods in Wineinformatics |
| title_short | Classification on Grade, Price, and Region with Multi-Label and Multi-Target Methods in Wineinformatics |
| title_sort | classification on grade price and region with multi label and multi target methods in wineinformatics |
| topic | classification informatics machine learning multi-label multi-target support vector machines wine wineinformatics |
| url | https://www.sciopen.com/article/10.26599/BDMA.2019.9020014 |
| work_keys_str_mv | AT jamespalmer classificationongradepriceandregionwithmultilabelandmultitargetmethodsinwineinformatics AT victorssheng classificationongradepriceandregionwithmultilabelandmultitargetmethodsinwineinformatics AT travisatkison classificationongradepriceandregionwithmultilabelandmultitargetmethodsinwineinformatics AT bernardchen classificationongradepriceandregionwithmultilabelandmultitargetmethodsinwineinformatics |