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Related Experiment Video

Updated: Jan 16, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

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Comparative analysis of algorithmic approaches in ensemble learning: bagging vs. boosting.

Hongke Zhao1,2, Wenhui Liu1,2, Yaxian Wang1,2

  • 1College of Management and Economics, Tianjin University, Tianjin, 300072, China.

Scientific Reports
|October 1, 2025
PubMed
Summary

This study introduces a theoretical model to compare Bagging and Boosting ensemble learning methods. Boosting offers higher performance but incurs greater computational costs than Bagging.

Related Experiment Videos

Last Updated: Jan 16, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.9K

Area of Science:

  • Machine Learning
  • Ensemble Methods
  • Computational Complexity

Background:

  • Bagging and Boosting are core ensemble learning algorithms widely used in practice.
  • Existing research primarily focuses on experimental performance comparisons, lacking theoretical analysis of benefits, costs, and complexities.
  • Algorithm-aware decision-making requires a deeper understanding of trade-offs between performance and resource utilization.

Purpose of the Study:

  • To develop and validate a theoretical model comparing Bagging and Boosting.
  • To quantify performance, computational costs, and ensemble complexity for both algorithms.
  • To provide practical guidance for selecting appropriate ensemble methods based on specific constraints.

Main Methods:

  • Development of a theoretical model for comparing Bagging and Boosting.
  • Empirical validation using four diverse datasets: MNIST, CIFAR-10, CIFAR-100, and IMDB.
  • Analysis across varying data complexities and computational environments.

Main Results:

  • Boosting demonstrates superior performance gains compared to Bagging, especially with increased ensemble complexity, but shows signs of overfitting.
  • Boosting requires significantly more computational time (e.g., ~14x) than Bagging for the same number of base learners.
  • Consistent trade-offs between performance and computational costs were observed across all datasets.

Conclusions:

  • The theoretical model's predictions are robust and validated by empirical results.
  • Bagging is recommended for cost-efficiency and complex datasets on high-performance devices.
  • Boosting is suitable for maximizing performance, simpler datasets, or average-performing devices.