Assessing Wine Quality using a Decision Tree
- Title
- Assessing Wine Quality using a Decision Tree
- Author
- 강경태
- Keywords
- Artificial intelligence; Decision trees; Learning systems; Statistical tests; Systems engineering; Machine learning repository; Methodical approach; Traditional assessment; Wine quality; Wine tasting; Wine
- Issue Date
- 2015-09
- Publisher
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- Citation
- 2015 IEEE International Symposium on Systems Engineering (ISSE), Page. 176-178
- 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.
- URI
- https://ieeexplore.ieee.org/document/7302752https://repository.hanyang.ac.kr/handle/20.500.11754/101470
- ISBN
- 978-1-4799-1920-8
- DOI
- 10.1109/SysEng.2015.7302752
- Appears in Collections:
- COLLEGE OF COMPUTING[E](소프트웨어융합대학) > COMPUTER SCIENCE(소프트웨어학부) > Articles
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