Wine Quality Assessment Using a Decision Tree with the Features Recommended by the Sequential Forward Selection (순차순방향선택 기반 특징 추출 및 의사나무를 이용한 와인 품질 측정)

Lee seunghan (이승한); Kyungtae Kang (강경태); Dong Kun Noh (노동건)

doi:10.9708/jksci.2017.22.02.081

Wine Quality Assessment Using a Decision Tree with the Features Recommended by the Sequential Forward Selection

Journal of The Korea Society of Computer and Information
Abbr : JKSCI
2017, 22(2), pp.81-87
DOI : 10.9708/jksci.2017.22.02.081
Publisher : The Korean Society Of Computer And Information
Research Area : Engineering > Computer Science
Received : December 6, 2016
Accepted : February 8, 2017
Published : February 28, 2017

Lee seunghan ¹, Kyungtae Kang ¹, Dong Kun Noh ²

¹한양대학교
²숭실대학교

Accredited

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.

KEYWORDS

Decision Tree, Wine Quality, Classification, Sequential Forward Selection

Citation status

* References for papers published after 2023 are currently being built.

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KJCKorea
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Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

Wine Quality Assessment Using a Decision Tree with the Features Recommended by the Sequential Forward Selection

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Citation status

* References for papers published after 2023 are currently being built.

Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

Wine Quality Assessment Using a Decision Tree with the Features Recommended by the Sequential Forward Selection

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KEYWORDS

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* References for papers published after 2023 are currently being built.