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<p>Class imbalance presents a critical challenge in machine learning applications, where conventional classifiers often exhibit systematic bias towards the majority class, significantly impairing minority-class classification accuracy. To address this issue, this paper proposes Convex Hull Delaunay Sampling (CHDS), a novel oversampling framework that combines boundary-oriented strategies with geometric space modeling. The proposed method operates in three key phases: (1) identifying boundary-proximal minority samples using local neighborhood entropy to enhance discriminative power and mitigate noise; (2) constructing convex hulls from boundary samples to delineate distribution space of the minority class, followed by Delaunay Triangulation and safe triangle region selection within these boundaries; (3) generating synthetic samples within secure triangular regions using a Dirichlet distribution-based strategy. Comprehensive evaluations across 28 imbalanced datasets reveal that CHDS consistently outperforms 15 resampling methods in terms of AUC, F1-score, G-mean, and Recall. Ablation studies further validate the individual contributions of each core component. The framework provides a reliable data-level solution for enhancing decision-making reliability in risk-sensitive applications characterized by class imbalance.</p>

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CHDS: a boundary-oriented oversampling method for imbalanced data based on convex hull and Delaunay triangulation

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月1日

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  • Chenlu Zheng,
  • Zixiang Zhu,
  • Jialin Liu

摘要

Class imbalance presents a critical challenge in machine learning applications, where conventional classifiers often exhibit systematic bias towards the majority class, significantly impairing minority-class classification accuracy. To address this issue, this paper proposes Convex Hull Delaunay Sampling (CHDS), a novel oversampling framework that combines boundary-oriented strategies with geometric space modeling. The proposed method operates in three key phases: (1) identifying boundary-proximal minority samples using local neighborhood entropy to enhance discriminative power and mitigate noise; (2) constructing convex hulls from boundary samples to delineate distribution space of the minority class, followed by Delaunay Triangulation and safe triangle region selection within these boundaries; (3) generating synthetic samples within secure triangular regions using a Dirichlet distribution-based strategy. Comprehensive evaluations across 28 imbalanced datasets reveal that CHDS consistently outperforms 15 resampling methods in terms of AUC, F1-score, G-mean, and Recall. Ablation studies further validate the individual contributions of each core component. The framework provides a reliable data-level solution for enhancing decision-making reliability in risk-sensitive applications characterized by class imbalance.