Weighted Ensemble with Dynamical Chunk Size for Imbalanced Data Streams in Nonstationary Environment
- 10.2991/iccia-17.2017.60How to use a DOI?
- Imbalanced data, concept drift, dynamic chunk, weighted ensemble
In recent years, learning from data stream has been more and more popular because of its extensive applications. However, most algorithms assume there are no concept drift in one chunk, as the performance of evaluation is sensitive to the chunk size. In this paper, we propose a new approach (WEDC) by introducing the concept drift detection mechanism to dynamically adjusting the chunk size. In addition, we add weighted mechanism to ensemble classifiers, which make WEDC could react to different types of concept drifts well. Experiments performed on the synthetic datasets show that our approach is competitive in the predication accuracy for data streams including different kinds of concept drifts.
- © 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/).
Cite this article
TY - CONF AU - Nini Liu AU - Wen Zhu AU - Bo Liao AU - Siqi Ren PY - 2016/07 DA - 2016/07 TI - Weighted Ensemble with Dynamical Chunk Size for Imbalanced Data Streams in Nonstationary Environment BT - Proceedings of the 2nd International Conference on Computer Engineering, Information Science & Application Technology (ICCIA 2017) PB - Atlantis Press SP - 352 EP - 355 SN - 2352-538X UR - https://doi.org/10.2991/iccia-17.2017.60 DO - 10.2991/iccia-17.2017.60 ID - Liu2016/07 ER -