A decision tree-based approach to mining the rules of concept drift

Chien I. Lee, Cheng Jung Tsai, Jhe Hao Wu, Wei Pang Yang

研究成果: Conference contribution

4 引文 斯高帕斯(Scopus)

摘要

In a database, the concept of an example might change along with time, which is known as concept drift. When the concept drift occurs, the classification model built by using old dataset is not suitable for predicting new coming dataset. Although many algorithms had been proposed to solve this problem, they focus only on updating the classification model. However, in a real life users might be very interested in the rules of concept drift. For example, doctors would desire to know the main causes more for disease variation since such rules would enable them to diagnose patients more correctly and quickly. In this paper, we propose a Concept Drift Rule mining Tree to accurately discover the rule of concept drift. The main contributions of this paper are: a) we address the problem of mining concept-drifting rule which was ignored in the past; b) our method can accurately mine the rule of concept drift.

原文English
主出版物標題Proceedings - Fourth International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2007
頁面639-643
頁數5
DOIs
出版狀態Published - 2007 十二月 1
事件4th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2007 - Haikou, China
持續時間: 2007 八月 242007 八月 27

出版系列

名字Proceedings - Fourth International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2007
4

Other

Other4th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2007
國家China
城市Haikou
期間07-08-2407-08-27

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Software
  • Applied Mathematics
  • Theoretical Computer Science

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