Name
Abstract | ||
|---|---|---|
Concept drift is a serious problem confronting machine learning systems in a dynamic and ever-changing world. In order to manage concept drift it may be useful to first quantify it by measuring the distance between distributions that generate data before and after a drift. There is a paucity of methods to do so in the case of multidimensional numeric data. This paper provides an in-depth analysis of the PCA-based change detection approach, identifies shortcomings of existing methods and shows how this approach can be used to measure a drift, not merely detect it. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/s10115-020-01438-3 | Knowledge and Information Systems |
Keywords | DocType | Volume |
Principal component analysis, Drift detection, Hellinger distance | Journal | 62 |
Issue | ISSN | Citations |
7 | 0219-1377 | 0 |
PageRank | References | Authors |
0.34 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Igor Goldenberg | 1 | 0 | 0.34 |
| Geoffrey I. Webb | 2 | 99 | 12.05 |