標題: A decision tree-based approach to mining the rules of concept drift
作者: Lee, Chien-, I
Tsai, Cheng-Jung
Wu, Jhe-Hao
Yang, Wei-Pang
資訊工程學系
Department of Computer Science
公開日期: 2007
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.
URI: http://dx.doi.org/10.1109/FSKD.2007.16
http://hdl.handle.net/11536/135157
DOI: 10.1109/FSKD.2007.16
期刊: FOURTH INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY, VOL 4, PROCEEDINGS
起始頁: 639
結束頁: +
Appears in Collections:Conferences Paper