Characterizing concept drift

• Published in: Data mining and knowledge discovery Vol. 30; no. 4; pp. 964 - 994
• Main Authors: Webb, Geoffrey I., Hyde, Roy, Cao, Hong, Nguyen, Hai Long, Petitjean, Francois
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.07.2016; Springer Nature B.V
• Subjects: Artificial Intelligence; Chemistry and Earth Sciences; Computer Science; Data mining; Data Mining and Knowledge Discovery; Descriptions; Distance learning; Drift; Foundations; Information Storage and Retrieval; Learning; Machine learning; Physics; Probability distribution; Quantitative analysis; Random variables; Statistics for Engineering; Taxonomy; Stream mining; Concept drift; Learning from non-stationary distributions; Stream learning

Most machine learning models are static, but the world is dynamic, and increasing online deployment of learned models gives increasing urgency to the development of efficient and effective mechanisms to address learning in the context of non-stationary distributions, or as it is commonly called conc...