Name
Abstract | ||
|---|---|---|
In this paper we study the problem of constructing accurate block-based ensemble classifiers from time evolving data streams. AWE is the best-known representative of these ensembles. We propose a new algorithm called Accuracy Updated Ensemble (AUE), which extends AWE by using online component classifiers and updating them according to the current distribution. Additional modifications of weighting functions solve problems with undesired classifier excluding seen in AWE. Experiments with several evolving data sets show that, while still requiring constant processing time and memory, AUE is more accurate than AWE. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/978-3-642-21222-2_19 | HAIS (2) |
Keywords | Field | DocType |
accuracy updated ensemble,best-known representative,current distribution,additional modification,concept drift,online component classifier,accurate block-based ensemble classifier,new algorithm,constant processing time,data stream | Data mining,Data set,Data stream mining,Weighting,Data stream,Computer science,Current distribution,Block (data storage),Concept drift,Artificial intelligence,Classifier (linguistics),Machine learning | Conference |
Volume | ISSN | Citations |
6679 | 0302-9743 | 40 |
PageRank | References | Authors |
1.22 | 8 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dariusz Brzezinski | 1 | 213 | 11.28 |
| Jerzy Stefanowski | 2 | 1653 | 139.25 |