为图分类器设计可自适应调节的类级正则化，提升抗噪与不平衡处理能力。
Large margin classifier with graph-based adaptive regularization
- 每类使用独立正则化超参数，动态调整分类边界
- 在边缘区域和存在异常值时表现更鲁棒，能有效剔除异常点
- 支持类别不平衡场景，通过灵活阈值优化提升性能
本文提出在基于Gabriel图的二分类器中引入每类独立的正则化超参数。我们分析了用于正则化的质量指标在分类边缘区域及存在异常值时的行为，并证明这种正则化灵活性可在训练过程中有效消除异常点。同时，该方法通过为多数类和少数类设置高低不同的阈值，缓解类别不平衡问题。相比固定阈值的单一解，灵活阈值扩展了解空间，可通过超参数优化算法进行调优。Friedman检验表明，灵活阈值能显著提升Gabriel图基分类器的性能。
原文摘要 · Abstract (English)
This paper introduces the use of per-class regularization hyperparameters in Gabriel graph-based binary classifiers. We demonstrate how the quality index used for regularization behaves both in the margin region and in the presence of outliers, and how incorporating this regularization flexibility can lead to solutions that effectively eliminate outliers while training the classifier. We also show how it can address class imbalance by generating higher and lower thresholds for the majority and minority classes, respectively. Thus, rather than having a single solution based on fixed thresholds, flexible thresholds expand the solution space and can be optimized through hyperparameter tuning algorithms. Friedman test shows that flexible thresholds are capable of improving Gabriel graph-based classifiers.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。