A Tree-based Concept Drift Detection Method by Three-way Decisions

DOI

10.2991/amcce-17.2017.28How to use a DOI?

Keywords

concept drift; three-way decisions; concept tree; data stream; uncertain factors.

Abstract

Concept drift detection is an active research area in data stream mining. The existing concept drift detection methods usually determine a concept is drifted or not drifted. In other words, the concepts are assigned into two classes such as drifted and un-drifted, which is typically based on two-way decisions. Most likely, these methods incorrectly determine that concept drift occurs due to some uncertain factors such as noise, and some real concept drifts are not detected. Inspired by the three-way decision theory, we propose a tree-based concept drift detection method by three-way decisions in this paper, in order to improve the accuracy of detection. The basic idea of the method is to assign the concepts into three classes such as drifted, nondeterministic drifted and un-drifted. Furthermore, concepts in the class of nondeterministic drifted are determined further according to the deviation between the classification error rates. The results of comparison experiments show the effectiveness and efficiency of the proposed methods.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - BaoHe Su
PY  - 2017/03
DA  - 2017/03
TI  - A Tree-based Concept Drift Detection Method by Three-way Decisions
BT  - Proceedings of the 2017 2nd International Conference on Automation, Mechanical Control and Computational Engineering (AMCCE 2017)
PB  - Atlantis Press
SP  - 157
EP  - 165
SN  - 2352-5401
UR  - https://doi.org/10.2991/amcce-17.2017.28
DO  - 10.2991/amcce-17.2017.28
ID  - Su2017/03
ER  -