Online Ensemble Using Adaptive Windowing for Data Streams with Concept Drift
Data streams, which can be considered as one of the primary sources of what is called big data, arrive continuously with high speed. The biggest challenge in data streams mining is to deal with concept drifts, during which ensemble methods are widely employed. The ensembles for handling concept drif...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2016-05-01
|
| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1155/2016/4218973 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849306033900486656 |
|---|---|
| author | Yange Sun Zhihai Wang Haiyang Liu Chao Du Jidong Yuan |
| author_facet | Yange Sun Zhihai Wang Haiyang Liu Chao Du Jidong Yuan |
| author_sort | Yange Sun |
| collection | DOAJ |
| description | Data streams, which can be considered as one of the primary sources of what is called big data, arrive continuously with high speed. The biggest challenge in data streams mining is to deal with concept drifts, during which ensemble methods are widely employed. The ensembles for handling concept drift can be categorized into two different approaches: online and block-based approaches. The primary disadvantage of the block-based ensembles lies in the difficulty of tuning the block size to provide a tradeoff between fast reactions to drifts. Motivated by this challenge, we put forward an online ensemble paradigm, which aims to combine the best elements of block-based weighting and online processing. The algorithm uses the adaptive windowing as a change detector. Once a change is detected, a new classifier is built replacing the worst one in the ensemble. By experimental evaluations on both synthetic and real-world datasets, our method performs significantly better than other ensemble approaches. |
| format | Article |
| id | doaj-art-312e55d16bcf4693b49bd413b0ec34e4 |
| institution | Kabale University |
| issn | 1550-1477 |
| language | English |
| publishDate | 2016-05-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-312e55d16bcf4693b49bd413b0ec34e42025-08-20T03:55:12ZengWileyInternational Journal of Distributed Sensor Networks1550-14772016-05-011210.1155/2016/4218973Online Ensemble Using Adaptive Windowing for Data Streams with Concept DriftYange Sun0Zhihai Wang1Haiyang Liu2Chao Du3Jidong Yuan4 School of Computer and Information Technology, Xinyang Normal University, Xinyang 464000, China School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, ChinaData streams, which can be considered as one of the primary sources of what is called big data, arrive continuously with high speed. The biggest challenge in data streams mining is to deal with concept drifts, during which ensemble methods are widely employed. The ensembles for handling concept drift can be categorized into two different approaches: online and block-based approaches. The primary disadvantage of the block-based ensembles lies in the difficulty of tuning the block size to provide a tradeoff between fast reactions to drifts. Motivated by this challenge, we put forward an online ensemble paradigm, which aims to combine the best elements of block-based weighting and online processing. The algorithm uses the adaptive windowing as a change detector. Once a change is detected, a new classifier is built replacing the worst one in the ensemble. By experimental evaluations on both synthetic and real-world datasets, our method performs significantly better than other ensemble approaches.https://doi.org/10.1155/2016/4218973 |
| spellingShingle | Yange Sun Zhihai Wang Haiyang Liu Chao Du Jidong Yuan Online Ensemble Using Adaptive Windowing for Data Streams with Concept Drift International Journal of Distributed Sensor Networks |
| title | Online Ensemble Using Adaptive Windowing for Data Streams with Concept Drift |
| title_full | Online Ensemble Using Adaptive Windowing for Data Streams with Concept Drift |
| title_fullStr | Online Ensemble Using Adaptive Windowing for Data Streams with Concept Drift |
| title_full_unstemmed | Online Ensemble Using Adaptive Windowing for Data Streams with Concept Drift |
| title_short | Online Ensemble Using Adaptive Windowing for Data Streams with Concept Drift |
| title_sort | online ensemble using adaptive windowing for data streams with concept drift |
| url | https://doi.org/10.1155/2016/4218973 |
| work_keys_str_mv | AT yangesun onlineensembleusingadaptivewindowingfordatastreamswithconceptdrift AT zhihaiwang onlineensembleusingadaptivewindowingfordatastreamswithconceptdrift AT haiyangliu onlineensembleusingadaptivewindowingfordatastreamswithconceptdrift AT chaodu onlineensembleusingadaptivewindowingfordatastreamswithconceptdrift AT jidongyuan onlineensembleusingadaptivewindowingfordatastreamswithconceptdrift |