Name
Abstract | ||
|---|---|---|
Concept drift and shift are major issues that greatly affect the accuracy and reliability of many real-world applications of machine learning. We propose a new data mining task, concept drift mapping—the description and analysis of instances of concept drift or shift. We argue that concept drift mapping is an essential prerequisite for tackling concept drift and shift. We propose tools for this purpose, arguing for the importance of quantitative descriptions of drift and shift in marginal distributions. We present quantitative concept drift mapping techniques, along with methods for visualizing their results. We illustrate their effectiveness for real-world applications across energy-pricing, vegetation monitoring and airline scheduling. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/s10618-018-0554-1 | Data Min. Knowl. Discov. |
Keywords | Field | DocType |
Concept drift,Concept shift,Non-stationary distribution,Visualisation,Mapping | Data mining,Scheduling (computing),Visualization,Computer science,Concept drift,Artificial intelligence,Marginal distribution,Machine learning,Quantitative Concept | Journal |
Volume | Issue | ISSN |
32 | 5 | 1384-5810 |
Citations | PageRank | References |
7 | 0.46 | 19 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Geoffrey I. Webb | 1 | 99 | 12.05 |
| Loong Kuan Lee | 2 | 7 | 0.46 |
| Bart Goethals | 3 | 1575 | 94.55 |
| François Petitjean | 4 | 474 | 34.26 |