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Domain Adaptive Bootstrap Aggregating.

Meimei Liu1, David B Dunson2

  • 1Department of Statistics, Virginia Tech, Blacksburg, VA 24061 USA.

IEEE Transactions on Signal Processing : a Publication of the IEEE Signal Processing Society
|March 6, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a domain adaptive bagging method to improve predictive algorithm performance when training and testing data distributions differ. The novel approach ensures bootstrap samples match testing data distributions, enhancing stability and accuracy.

Keywords:
Baggingclassificationdomain adaptationensemble learninggeneralizability

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Area of Science:

  • Machine Learning
  • Data Science
  • Computer Science

Background:

  • Predictive algorithm performance degrades with distributional shifts between training and testing data (domain adaptation problem).
  • Bootstrap aggregating (bagging) enhances algorithm stability, reduces variance, and prevents overfitting.

Purpose of the Study:

  • To propose a novel domain adaptive bagging method to address the domain adaptation problem.
  • To enhance the stability and accuracy of predictive algorithms in the presence of distributional shifts.

Main Methods:

  • Developed a domain adaptive bagging method integrated with an iterative nearest neighbor sampler.
  • Bootstrap samples are drawn to match the distribution of new testing data.
  • Modified the method to accommodate anomalous samples (outliers) in test data.

Main Results:

  • The proposed approach offers a general ensemble framework applicable to various classifiers and complex domains, including manifolds.
  • Demonstrated effectiveness through theoretical support, simulations, and real-data applications.
  • Successfully adapted algorithms to distributional shifts and handled anomalous test data.

Conclusions:

  • The domain adaptive bagging method effectively mitigates performance degradation due to distributional shifts.
  • The iterative nearest neighbor sampler is key to aligning bootstrap sample distributions with test data.
  • This framework provides a robust solution for domain adaptation in machine learning.