Publicação: An Overview on Concepts Drift Learning
Carregando...
Data
Orientador
Coorientador
Pós-graduação
Curso de graduação
Título da Revista
ISSN da Revista
Título de Volume
Editor
Ieee-inst Electrical Electronics Engineers Inc
Tipo
Artigo
Direito de acesso
Acesso aberto
Resumo
Concept drift techniques aim at learning patterns from data streams that may change over time. Although such behavior is not usually expected in controlled environments, real-world scenarios can face changes in the data, such as new classes, clusters, and features. Traditional classifiers can be easily fooled in such situations, resulting in poor performances. Common concept drift domains include recommendation systems, energy consumption, artificial intelligence systems with dynamic environment interaction, and biomedical signal analysis (e.g., neurogenerative diseases). In this paper, we surveyed several works that deal with concept drift, as well as we presented a comprehensive study of public synthetic and real datasets that can be used to cope with such a problem. In addition, we considered a review of different types of drifts and approaches to handling such changes in the data. We considered different learners employed in classification tasks and the use of drift detection mechanisms, among other characteristics.
Descrição
Palavras-chave
Concept drift, machine learning, pattern recognition
Idioma
Inglês
Como citar
Ieee Access. Piscataway: Ieee-inst Electrical Electronics Engineers Inc, v. 7, p. 1532-1547, 2019.
