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Abstract <p>Constructing convex combinations of predictors is an effective method for building ensembles for solving regression problems. Moreover, it seems possible to improve the final quality of the algorithm if the initial set of predictors is constructed in a special way. In this paper, we study two techniques that allow us to achieve such an improvement: bagging in combination with the random subspace method (RSM), and optimization of the divergence of predictors. The effectiveness of the resulting methods is verified in applied problems.</p>

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Methods for Constructing Predictor Ensembles Based on Convex Combinations

  • 期刊论文

  • 发表日期：

    2025年8月1日

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  • I. M. Borisov,
  • A. A. Dokukin,
  • O. V. Senko

摘要

Abstract

Constructing convex combinations of predictors is an effective method for building ensembles for solving regression problems. Moreover, it seems possible to improve the final quality of the algorithm if the initial set of predictors is constructed in a special way. In this paper, we study two techniques that allow us to achieve such an improvement: bagging in combination with the random subspace method (RSM), and optimization of the divergence of predictors. The effectiveness of the resulting methods is verified in applied problems.