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

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationProceedings - Fourth International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2007
Pages639-643
Number of pages5
DOIs
Publication statusPublished - 2007 Dec 1
Event4th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2007 - Haikou, China
Duration: 2007 Aug 242007 Aug 27

Publication series

NameProceedings - Fourth International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2007
Volume4

Other

Other4th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2007
CountryChina
CityHaikou
Period07-08-2407-08-27

Fingerprint

Concept Drift
Decision trees
Decision tree
Mining
Updating
Model

All Science Journal Classification (ASJC) codes

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

Cite this

Lee, C. I., Tsai, C. J., Wu, J. H., & Yang, W. P. (2007). A decision tree-based approach to mining the rules of concept drift. In Proceedings - Fourth International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2007 (pp. 639-643). [4406458] (Proceedings - Fourth International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2007; Vol. 4). https://doi.org/10.1109/FSKD.2007.16
Lee, Chien I. ; Tsai, Cheng Jung ; Wu, Jhe Hao ; Yang, Wei Pang. / A decision tree-based approach to mining the rules of concept drift. Proceedings - Fourth International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2007. 2007. pp. 639-643 (Proceedings - Fourth International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2007).
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Lee, CI, Tsai, CJ, Wu, JH & Yang, WP 2007, A decision tree-based approach to mining the rules of concept drift. in Proceedings - Fourth International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2007., 4406458, Proceedings - Fourth International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2007, vol. 4, pp. 639-643, 4th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2007, Haikou, China, 07-08-24. https://doi.org/10.1109/FSKD.2007.16

A decision tree-based approach to mining the rules of concept drift. / Lee, Chien I.; Tsai, Cheng Jung; Wu, Jhe Hao; Yang, Wei Pang.

Proceedings - Fourth International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2007. 2007. p. 639-643 4406458 (Proceedings - Fourth International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2007; Vol. 4).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Lee CI, Tsai CJ, Wu JH, Yang WP. A decision tree-based approach to mining the rules of concept drift. In Proceedings - Fourth International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2007. 2007. p. 639-643. 4406458. (Proceedings - Fourth International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2007). https://doi.org/10.1109/FSKD.2007.16