An Overview on Concepts Drift Learning

Autor(es)

Iwashita, Adriana Sayuri ^[1] ; Papa, Joao Paulo ^[2]

Afiliação do(s) autor(es)

^[1] Univ Fed Sao Carlos, Dept Comp, BR-13565905 Sao Carlos, SP - Brazil

^[2] Sao Paulo State Univ, Dept Comp, BR-17033360 Bauru - Brazil

• Tipo de documento: Artigo Científico
• Fonte: IEEE ACCESS; v. 7, p. 1532-1547, 2019.
• Citações Web of Science: 0

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. (AU)

• Processo FAPESP: 14/16250-9 - Sobre a otimização de parâmetros em técnicas de aprendizado de máquina: avanços e paradigmas
• Beneficiário: João Paulo Papa
• Linha de fomento: Auxílio à Pesquisa - Regular
• Processo FAPESP: 14/12236-1 - AnImaLS: Anotação de Imagem em Larga Escala: o que máquinas e especialistas podem aprender interagindo?
• Beneficiário: Alexandre Xavier Falcão
• Linha de fomento: Auxílio à Pesquisa - Temático
• Processo FAPESP: 13/07375-0 - CeMEAI - Centro de Ciências Matemáticas Aplicadas à Indústria
• Beneficiário: José Alberto Cuminato
• Linha de fomento: Auxílio à Pesquisa - Centros de Pesquisa, Inovação e Difusão - CEPIDs
• Processo FAPESP: 16/19403-6 - Modelos de aprendizado baseados em energia e suas aplicações
• Beneficiário: João Paulo Papa
• Linha de fomento: Auxílio à Pesquisa - Regular