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Toward Efficient Ensemble Learning with Structure Constraints: Convergent Algorithms and Applications

Author

Listed:
  • Shao-Bo Lin

    (Center for Intelligent Decision-Making and Machine Learning, School of Management, Xi’an Jiaotong University, Xi’an, Shanxi 710049, China)

  • Shaojie Tang

    (Naveen Jindal School of Management, University of Texas at Dallas, Richardson, Texas 75083)

  • Yao Wang

    (Center for Intelligent Decision-Making and Machine Learning, School of Management, Xi’an Jiaotong University, Xi’an, Shanxi 710049, China)

  • Di Wang

    (Center for Intelligent Decision-Making and Machine Learning, School of Management, Xi’an Jiaotong University, Xi’an, Shanxi 710049, China)

Abstract

Ensemble learning methods, such as boosting, focus on producing a strong classifier based on numerous weak classifiers. In this paper, we develop a novel ensemble learning method called rescaled boosting with truncation (ReBooT) for binary classification by combining well-known rescaling and regularization ideas in boosting. Theoretically, we present some sufficient conditions for the convergence of ReBooT, derive an almost optimal numerical convergence rate, and deduce fast-learning rates in the framework of statistical learning theory. Experimentally, we conduct both toy simulations and four real-world data runs to show the power of ReBooT. Our results show that, compared with the existing boosting algorithms, ReBooT possesses better learning performance and interpretability in terms of solid theoretical guarantees, perfect structure constraints, and good prediction performance.

Suggested Citation

  • Shao-Bo Lin & Shaojie Tang & Yao Wang & Di Wang, 2022. "Toward Efficient Ensemble Learning with Structure Constraints: Convergent Algorithms and Applications," INFORMS Journal on Computing, INFORMS, vol. 34(6), pages 3096-3116, November.
    • Handle: RePEc:inm:orijoc:v:34:y:2022:i:6:p:3096-3116
    DOI: 10.1287/ijoc.2022.1224
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    References listed on IDEAS

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