Name
Abstract | ||
|---|---|---|
•Large-scale comparison of 14 concept drift detectors for mining data streams.•Aims to measure how good the existent concept drift detectors really are.•Challenges a common belief in the area regarding the best drift detectors.•Most well-known/cited methods were consistently among the worst configurations.•May also be seen as an extensive literature survey of concept drift detectors. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.ins.2018.04.014 | Information Sciences |
Keywords | Field | DocType |
Concept drift,Drift detection,Large-scale comparison,Data stream,Online learning | Online learning,Naive Bayes classifier,Concept drift,Artificial intelligence,Labeled data,Drift detection,Detector,Mathematics,Machine learning | Journal |
Volume | ISSN | Citations |
451 | 0020-0255 | 8 |
PageRank | References | Authors |
0.53 | 28 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Roberto Souto Maior de Barros | 1 | 19 | 1.07 |
| Silas Garrido Teixeira de Carvalho Santos | 2 | 54 | 5.01 |