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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 :https://doi.org/10.9708/jksci.2017.22.02.081
Publisher : The Korean Society Of Computer And Information
Research Area : Computer Science
2017, 22(2), pp.81-87
DOI :https://doi.org/10.9708/jksci.2017.22.02.081
Publisher : The Korean Society Of Computer And Information
Research Area : Computer Science
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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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