Classification of Wine Quality Using a Decision Tree with Sequential Forward Selection

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. Popularization of the current wine market has been growing rapidly. Now many consumers have an interest in the quality of the wine. The recently increase in the consumption of wine became familiar to consumers, but recognition of the wine quality information it does not familiar. Therefore, consumers are sharing information exchange and social networking to meet the needs for wine information. However, there can arise if you don't acquire accurate information due to the general description of the people who are not wine experts. The taste of a trained panelist currently gives more informed results, but this method of quality measurement is time-consuming and expensive. 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. A new approach, in which a decision tree is used to infer quality, as perceived by a wine tester from physiochemical characteristics. And it analyzes a data feature and it considers it uses each quality and Sequential Forward Selection.