Projects per year
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.
Original language | English |
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Pages (from-to) | 1179-1199 |
Number of pages | 21 |
Journal | Data Mining and Knowledge Discovery |
Volume | 32 |
Issue number | 5 |
DOIs | |
Publication status | Published - Sept 2018 |
Keywords
- Concept drift
- Concept shift
- Mapping
- Non-stationary distribution
- Visualisation
Projects
- 2 Finished
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Time series classification for new-generation Earth observation satellites
Petitjean, F. (Primary Chief Investigator (PCI))
1/06/17 → 31/12/20
Project: Research
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Combining generative and discriminative strategies to facilitate efficient and effective learning from big data
Webb, G. (Primary Chief Investigator (PCI))
ARC - Australian Research Council, Monash University
2/01/14 → 31/12/16
Project: Research