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      On ensemble techniques for data stream regression

      Gomes, Heitor Murilo; Montiel, Jacob; Mastelini, Saulo Martiello; Pfahringer, Bernhard; Bifet, Albert
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      On Ensemble Techniques apepr.pdf
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      DOI
       10.1109/IJCNN48605.2020.9206756
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      Gomes, H. M., Montiel, J., Mastelini, S. M., Pfahringer, B., & Bifet, A. (2020). On ensemble techniques for data stream regression. In Proceedings of 2020 International Joint Conference on Neural Networks (IJCNN). Washington, DC, USA: IEEE. https://doi.org/10.1109/IJCNN48605.2020.9206756
      Permanent Research Commons link: https://hdl.handle.net/10289/14237
      Abstract
      An ensemble of learners tends to exceed the predictive performance of individual learners. This approach has been explored for both batch and online learning. Ensembles methods applied to data stream classification were thoroughly investigated over the years, while their regression counterparts received less attention in comparison. In this work, we discuss and analyze several techniques for generating, aggregating, and updating ensembles of regressors for evolving data streams. We investigate the impact of different strategies for inducing diversity into the ensemble by randomizing the input data (resampling, random subspaces and random patches). On top of that, we devote particular attention to techniques that adapt the ensemble model in response to concept drifts, including adaptive window approaches, fixed periodical resets and randomly determined windows. Extensive empirical experiments show that simple techniques can obtain similar predictive performance to sophisticated algorithms that rely on reactive adaptation (i.e., concept drift detection and recovery).
      Date
      2020
      Type
      Conference Contribution
      Publisher
      IEEE
      Rights
      © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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      • Computing and Mathematical Sciences Papers [1436]
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