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Assessing wine quality using a decision tree
- Authors
- Lee, Seunghan; Park, Juyoung; Kang, Kyungtae
- Issue Date
- Sep-2015
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Keywords
- Artificial intelligence; Decision trees; Learning systems; Statistical tests; Systems engineering; Machine learning repository; Methodical approach; Traditional assessment; Wine quality; Wine tasting; Wine
- Citation
- 1st IEEE International Symposium on Systems Engineering, ISSE 2015 - Proceedings, pp.176 - 178
- Indexed
- OTHER
- Journal Title
- 1st IEEE International Symposium on Systems Engineering, ISSE 2015 - Proceedings
- Start Page
- 176
- End Page
- 178
- Abstract
- Even though wine-drinkers generally agree that wines may be ranked by quality, wine-tasting is famously subjective. There have been many attempts to construct a more methodical approach to the assessment of wines. We propose a method of assessing wine quality using a decision tree, and test it against the wine-quality dataset from the UC Irvine Machine Learning Repository. Results are 60% in agreement with traditional assessment techniques. © 2015 IEEE.
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Related Researcher
- Kang, Kyung tae
- COLLEGE OF COMPUTING (DEPARTMENT OF ARTIFICIAL INTELLIGENCE)