Challenges on Classifying Data Streams with Concept Drift

  • Eduardo Victor Lima Barboza Universidade Federal do Paraná
  • Paulo Ricardo Lisboa de Almeida Universidade Federal do Paraná


Concept Drift é um problema comum quando estamos trabalhando com Aprendizado de Máquina. Refere-se a uma mudança de conceito em um intervalo de tempo, o que pode deteriorar a acurácia do modelo. Um problema recorrente em concept drift é achar datasets que reflitam cenários do mundo real. Neste trabalho, mostramos algumas bases de dados, onde sabe-se que existe Concept Drift, e propomos algumas mudanças em um método existente (Dynse), que inclui fazê-lo capaz de lidar com fluxos de dados, ao invés de lotes, e colocar algum gatilho nele, para deixar sua janela adaptativa, com detecção de concept drift.

Palavras-chave: Concept Drift, Data Streams, Datasets, Machine Learning


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BARBOZA, Eduardo Victor Lima; DE ALMEIDA, Paulo Ricardo Lisboa. Challenges on Classifying Data Streams with Concept Drift. In: WORKSHOP DE TESES E DISSERTAÇÕES (WTDBD) - SIMPÓSIO BRASILEIRO DE BANCO DE DADOS (SBBD), 37. , 2022, Búzios. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2022 . p. 126-132. DOI: