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      A survey on feature drift adaptation: Definition, benchmark, challenges and future directions

      Barddal, Jean Paul; Gomes, Heitor Murilo; Enembreck, Fabrício; Pfahringer, Bernhard
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      draft_survey_jss.pdf
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      DOI
       10.1016/j.jss.2016.07.005
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      Barddal, J. P., Gomes, H. M., Enembreck, F., & Pfahringer, B. (2017). A survey on feature drift adaptation: Definition, benchmark, challenges and future directions. Journal of Systems and Software, 127, 278–294. https://doi.org/10.1016/j.jss.2016.07.005
      Permanent Research Commons link: https://hdl.handle.net/10289/11221
      Abstract
      Data stream mining is a fast growing research topic due to the ubiquity of data in several real-world problems. Given their ephemeral nature, data stream sources are expected to undergo changes in data distribution, a phenomenon called concept drift. This paper focuses on one specific type of drift that has not yet been thoroughly studied, namely feature drift. Feature drift occurs whenever a subset of features becomes, or ceases to be, relevant to the learning task; thus, learners must detect and adapt to these changes accordingly. We survey existing work on feature drift adaptation with both explicit and implicit approaches. Additionally, we benchmark several algorithms and a naive feature drift detection approach using synthetic and real-world datasets. The results from our experiments indicate the need for future research in this area as even naive approaches produced gains in accuracy while reducing resources usage. Finally, we state current research topics, challenges and future directions for feature drift adaptation.
      Date
      2017
      Type
      Journal Article
      Publisher
      Elsevier
      Collections
      • Computing and Mathematical Sciences Papers [1445]
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