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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/7302752http://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] > COMPUTER SCIENCE(소프트웨어학부) > Articles
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