• DocumentCode
    3674921
  • Title

    Assessing wine quality using a decision tree

  • Author

    Seunghan Lee;Juyoung Park;Kyungtae Kang

  • Author_Institution
    Dept. of Computer Science &
  • fYear
    2015
  • Firstpage
    176
  • Lastpage
    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.
  • Keywords
    "Decision trees","Accuracy","Data mining","Sulfur","Conferences","Predictive models","Data models"
  • Publisher
    ieee
  • Conference_Titel
    Systems Engineering (ISSE), 2015 IEEE International Symposium on
  • Type

    conf

  • DOI
    10.1109/SysEng.2015.7302752
  • Filename
    7302752
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3674921