Online Adaptation in Learning Classifier Systems : Stream Data Mining

@inproceedings{Abbass2004OnlineAI,
  title={Online Adaptation in Learning Classifier Systems : Stream Data Mining},
  author={Hussein A. Abbass and Jaume Bacardit and Martin V. Butz and Xavier Llor{\`a} IlliGAL},
  year={2004}
}
In data mining, concept drift refers to the phenomenon that the underlying model (or concept) is changing over time. The aim of this paper is twofold. First, we propose a fundamental characterization and quantification of different types of concept drift. The proposed theory enables a rigorous investigation of learning system performance on streamed data. In particular , we investigate the impact of different amounts and types of concept drift on evolutionary classification systems focusing on… CONTINUE READING
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