Wine Quality Assessment Using a Decision Tree with the Features Recommended by the Sequential Forward Selection
- Title
- Wine Quality Assessment Using a Decision Tree with the Features Recommended by the Sequential Forward Selection
- Other Titles
- 순차순방향선택 기반 특징 추출 및 의사나무를 이용한 와인 품질 측정
- Author
- 강경태
- Keywords
- Decision Tree; Wine Quality; Classification; Sequential Forward Selection
- Issue Date
- 2017-02
- Publisher
- 한국컴퓨터정보학회
- Citation
- 한국컴퓨터정보학회논문지, v. 22, No. 2, Page. 81-87
- Abstract
- Nowadays wine is increasingly enjoyed by a wider range of consumers, and wine certification and quality assessment are key elements in supporting the wine industry to develop new technologies for both wine making and selling processes. There have been many attempts to construct a more methodical approach to the assessment of wines, but most of them rely on objective decision rather than subjective judgement. In this paper, we propose a data mining approach to predict human wine taste preferences that is based on easily available analytical tests at the certification step. We used sequential forward selection and decision tree for this purpose. Experiments with the wine quality dataset from the UC Irvine Machine Learning Repository demonstrate the accuracies of 76.7% and 78.7% for red and white wines respectively.
- URI
- http://www.dbpia.co.kr/Journal/ArticleDetail/NODE07113046https://repository.hanyang.ac.kr/handle/20.500.11754/103149
- ISSN
- 1598-849X
- Appears in Collections:
- COLLEGE OF COMPUTING[E](소프트웨어융합대학) > COMPUTER SCIENCE(소프트웨어학부) > Articles
- Files in This Item:
There are no files associated with this item.
- Export
- RIS (EndNote)
- XLS (Excel)
- XML