An aggregate ensemble for mining concept drifting data streams with noise
- Publication Type:
- Journal Article
- Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2009, 5476 LNAI pp. 1021 - 1029
- Issue Date:
Copyright Clearance Process
- Recently Added
- In Progress
- Closed Access
This item is closed access and not available.
Recent years have witnessed a large body of research work on mining concept drifting data streams, where a primary assumption is that the up-to-date data chunk and the yet-to-come data chunk share identical distributions, so classifiers with good performance on the up-to-date chunk would also have a good prediction accuracy on the yet-to-come data chunk. This "stationary assumption", however, does not capture the concept drifting reality in data streams. More recently, a "learnable assumption" has been proposed and allows the distribution of each data chunk to evolve randomly. Although this assumption is capable of describing the concept drifting in data streams, it is still inadequate to represent realworld data streams which usually suffer from noisy data as well as the drifting concepts. In this paper, we propose a Realistic Assumption which asserts that the difficulties of mining data streams are mainly caused by both concept drifting and noisy data chunks. Consequently, we present a new Aggregate Ensemble (AE) framework, which trains base classifiers using different learning algorithms on different data chunks. All the base classifiers are then combined to form a classifier ensemble through model averaging. Experimental results on synthetic and real-world data show that AE is superior to other ensemble methods under our new realistic assumption for noisy data streams. © Springer-Verlag Berlin Heidelberg 2009.
Please use this identifier to cite or link to this item: