A survey on concept drift adaptation
Universidade do Porto · Aalto University · +3 more institutions
Abstract
Concept drift primarily refers to an online supervised learning scenario when the relation between the input data and the target variable changes over time. Assuming a general knowledge of supervised learning in this article, we characterize adaptive learning processes; categorize existing strategies for handling concept drift; overview the most representative, distinct, and popular techniques and algorithms; discuss evaluation methodology of adaptive algorithms; and present a set of illustrative applications. The survey covers the different facets of concept drift in an integrated way to reflect on the existing scattered state of the art. Thus, it aims at providing a comprehensive introduction to the concept…
Citation impact
- FWCI
- 196.18
- Percentile
- 100%
- References
- 172
Authors
5Topics & keywords
- Concept drift
- Computer science
- Categorization
- Adaptation (eye)
- Relation (database)
- Set (abstract data type)
- Machine learning
- Artificial intelligence
- Industry, innovation and infrastructure