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Abstract <p>This study presents the results of an experimental analysis evaluating the effectiveness of Extra Trees within gradient boosting models, as well as in a newly proposed ensemble framework where the forest is generated under conditions of enhanced internal divergence. Additionally, the paper explores the performance of extra trees when applied to novel feature representations computed as IDO distances to a selected set of reference examples. It has been shown that the use of extra randomized trees in gradient boosting and divergent forest models improves generalization ability. The use of expanded feature sets leads to even greater generalization ability.</p>

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Some Approaches to Improving Prediction Accuracy Using Ensemble Methods

  • 期刊论文

  • 发表日期：

    2025年12月1日

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  • X. Ma,
  • O. V. Senko

摘要

Abstract

This study presents the results of an experimental analysis evaluating the effectiveness of Extra Trees within gradient boosting models, as well as in a newly proposed ensemble framework where the forest is generated under conditions of enhanced internal divergence. Additionally, the paper explores the performance of extra trees when applied to novel feature representations computed as IDO distances to a selected set of reference examples. It has been shown that the use of extra randomized trees in gradient boosting and divergent forest models improves generalization ability. The use of expanded feature sets leads to even greater generalization ability.