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Related Experiment Videos

The optimal crowd learning machine.

Bilguunzaya Battogtokh1, Majid Mojirsheibani2, James Malley1

  • 1Center for Information Technology, National Institutes of Health, Bethesda, MD USA.

Biodata Mining
|May 24, 2017
PubMed
Summary
This summary is machine-generated.

Combining multiple learning machines, known as an Optimal Crowd, offers provable prediction accuracy. This ensemble method is at least as good as the best individual machine, especially with large datasets.

Related Experiment Videos

Area of Science:

  • Machine Learning
  • Ensemble Methods
  • Statistical Learning Theory

Background:

  • Various methods exist for combining multiple learning machines into a single model.
  • The usefulness of these ensemble methods varies significantly.

Purpose of the Study:

  • To introduce and analyze the 'Optimal Crowd' concept for machine learning ensembles.
  • To establish theoretical guarantees for the performance of Optimal Crowds.

Main Methods:

  • Theoretical analysis of ensemble learning.
  • Demonstration using real-world data from the UCI machine learning repository.

Main Results:

  • An Optimal Crowd is provably at least as accurate as the best machine in its family for predictions.
  • If a single machine in the family is Bayes optimal, the Optimal Crowd is also asymptotically Bayes optimal with sufficient data.

Conclusions:

  • The optimality of the Optimal Crowd relies only on the boundedness of the outcome variable.
  • The proposed scheme is validated with practical data, and potential future research directions are outlined.