Open AccessDriftage: a multi-agent system framework for concept drift detection
Author(s) -
Diogo Munaro Vieira,
Chrystinne Oliveira Fernandes,
Carlos Lucena,
Sérgio Lifschitz
Publication year - 2021
Publication title -
gigascience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.947
H-Index - 54
ISSN - 2047-217X
DOI - 10.1093/gigascience/giab030
Subject(s) - interpretability , concept drift , computer science , pace , adaptation (eye) , interactivity , artificial intelligence , detector , reduction (mathematics) , machine learning , data mining , data stream mining , telecommunications , multimedia , physics , geometry , mathematics , geodesy , optics , geography
The amount of data and behavior changes in society happens at a swift pace in this interconnected world. Consequently, machine learning algorithms lose accuracy because they do not know these new patterns. This change in the data pattern is known as concept drift. There exist many approaches for dealing with these drifts. Usually, these methods are costly to implement because they require (i) knowledge of drift detection algorithms, (ii) software engineering strategies, and (iii) continuous maintenance concerning new drifts.