Seasonal Averaged One-Dependence Estimators: A Novel Algorithm to Address Seasonal Concept Drift in High-Dimensional Stream Classification

@article{Godahewa2020SeasonalAO,
  title={Seasonal Averaged One-Dependence Estimators: A Novel Algorithm to Address Seasonal Concept Drift in High-Dimensional Stream Classification},
  author={Rakshitha Godahewa and Trevor Yann and C. Bergmeir and François Petitjean},
  journal={2020 International Joint Conference on Neural Networks (IJCNN)},
  year={2020},
  pages={1-8}
}
Stream classification methods classify a continuous stream of data as new labelled samples arrive. They often also have to deal with concept drift. This paper focuses on seasonal drift in stream classification, which can be found in many real-world application data sources. Traditional approaches of stream classification consider seasonal drift by including seasonal dummy/indicator variables or building separate models for each season. But these approaches have strong limitations in high… 
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