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Related Experiment Video

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An R-Based Landscape Validation of a Competing Risk Model
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Online cross-validation-based ensemble learning.

David Benkeser1, Cheng Ju1, Sam Lendle1

  • 1Group in Biostatistics, University of California, Berkeley, Berkeley 101 Haviland HallCA, U.S.A.

Statistics in Medicine
|May 6, 2017
PubMed
Summary

This study introduces flexible, ensemble-based online estimators for big data. These methods use online cross-validation to select the best algorithm, ensuring scalable and accurate streaming estimates for various models.

Keywords:
cross-validationdependent data ensemble learningmachine learningonline estimationstochastic gradient descenttime-series

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Area of Science:

  • Machine Learning
  • Statistical Modeling
  • Big Data Analytics

Background:

  • Online estimators provide scalable big data solutions by updating estimates with new data sequentially.
  • Existing methods may struggle with infinite-dimensional parameters and complex data generation processes.

Purpose of the Study:

  • To develop flexible, ensemble-based online estimators for infinite-dimensional parameters.
  • To enable accurate streaming estimates in settings with sequentially generated data.
  • To outperform existing online estimation techniques through adaptive algorithm selection.

Main Methods:

  • Ensemble-based online estimation framework.
  • Utilizing a library of candidate online estimators.
  • Employing online cross-validation for optimal algorithm selection.
  • Developing extensions for optimal ensemble estimation.

Main Results:

  • Asymptotic performance guarantee matching the best unknown algorithm.
  • Demonstrated excellent performance via simulations.
  • Successful application in real-world streaming predictions of infectious disease incidence.

Conclusions:

  • The proposed ensemble-based online estimators offer a robust and scalable approach for big data.
  • Online cross-validation effectively identifies high-performing algorithms in streaming data settings.
  • The methods show promise for real-time prediction tasks, such as public health surveillance.