Name
Abstract | ||
|---|---|---|
•This paper proposes a proactive approach for abrupt drift detection, called DetectA (Detect Abrupt Drift).•DetectA label the patterns from the test set using an unsupervised method and compare the train and test statistics using a hypothesis test.•To perform a sensitivity analysis of the DetectA model, a procedure for creating datasets with abrupt drift has been proposed.•The sensitivity analysis suggests that DetectA is suitable for high-dimensional and imbalanced datasets.•In general, the proactive manner a top contender in terms of improving the underlying base classifier accuracy. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.asoc.2017.10.031 | Applied Soft Computing |
Keywords | Field | DocType |
Concept drift,Drift detection,Proactive approach | Data mining,Computer science,Multivariate statistics,Block (data storage),Concept drift,Artificial intelligence,Drift detection,Classifier (linguistics),Detector,Machine learning,Statistical hypothesis testing,Test set | Journal |
Volume | ISSN | Citations |
62 | 1568-4946 | 6 |
PageRank | References | Authors |
0.42 | 19 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tatiana Escovedo | 1 | 17 | 3.72 |
| Adriano Soares Koshiyama | 2 | 34 | 10.19 |
| André Vargas Abs da Cruz | 3 | 29 | 5.91 |
| Marley M. B. R. Vellasco | 4 | 6 | 1.43 |