Name
Abstract | ||
|---|---|---|
Deployed machine learning systems are necessarily learned from historical data and are often applied to current data. When the world changes, the learned models can lose fidelity. Such changes to the statistical properties of data over time are known as concept drift. Similarly, models are often learned in one context, but need to be applied in another. This is called concept shift. Quantifying the magnitude of drift or shift, especially in the context of covariate drift or shift, or unsupervised learning, requires use of measures of distance between distributions. In this paper, we survey such distance measures with respect to their suitability for estimating drift and shift magnitude between samples of numeric data. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/s10115-018-1257-z | Knowledge and Information Systems |
Keywords | Field | DocType |
Multivariate concept drift,Mahalanobis distance,Hotelling distance,Hellinger distance,Kullback–Leibler divergence | Covariate,Hellinger distance,Computer science,Concept drift,Mahalanobis distance,Unsupervised learning,Artificial intelligence,Statistical distance,Kullback–Leibler divergence,Machine learning,Distance measures | Journal |
Volume | Issue | ISSN |
60 | 2 | 0219-3116 |
Citations | PageRank | References |
2 | 0.36 | 7 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Igor Goldenberg | 1 | 2 | 0.36 |
| Geoffrey I. Webb | 2 | 3130 | 234.10 |