Diagnosing the quality of wine using an adapting decision tree classifier for streaming data

Tóm tắt

The research is focused on exploring the applications of Artificial Intelligence algorithms in handling diagnostic wine quality data. The article discusses the successful implementation of the Decision Tree algorithm for this purpose. This drives the main research goal, which revolves around integrating the Decision Tree with flexible sliding window techniques that can continuously adapt and update over time. The primary objective of the study is to address the wine quality diagnostic problem. Alongside this goal, there are additional smaller objectives to achieve. The initial step involves studying and researching theoretical foundations and measurement methods, as well as analyzing wine quality. Lastly, the goal of deploying a test application is set, aiming to create a Wine Quality Diagnostic Page. The interface of the page is designed to be user-friendly, intuitive, and informative about the functioning and content of the wine quality diagnostic method.