Journal of the Korea Society of Computer and Information (한국컴퓨터정보학회논문지)

Korean Society of Computer Information (한국컴퓨터정보학회)
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Wine Quality Assessment Using a Decision Tree with the Features Recommended by the Sequential Forward Selection

  • Lee, Seunghan (Dept. of Computer Science and Engineering, Hanyang University) ;
  • Kang, Kyungtae (Dept. of Computer Science and Engineering, Hanyang University) ;
  • Noh, Dong Kun (Department of Smart Systems SW, Soongsil University)
  • Received : 2016.12.06
  • Accepted : 2017.02.08
  • Published : 2017.02.28
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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.

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References

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