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English(EN) Instance Hardness-Based Relevance for Imbalanced Regression

新框架应对不平衡回归挑战 · 2篇论文

两篇新研究论文解决了不平衡回归的挑战,即目标变量中的某些值范围代表性不足。第一篇论文《基于实例难度的不平衡回归相关性》介绍了InHaR函数,该函数不仅从目标值推断稀有度,还从学习难度推断稀有度,在重采样策略下表现出性能提升。第二篇论文《DADIR:面向不平衡回归的感知数据级不平衡回归框架》提出了一个使用密度信息划分目标空间并生成合成数据的框架,在预测性能方面,尤其是在代表性不足的区域,显示出了一致的改进。 AI

影响 这些新方法可以提高机器学习模型在数据分布不均情况下的准确性,从而在实际应用中获得更好的性能。

排序理由 两篇在arXiv上发表的学术论文,提出了不平衡回归的新方法。

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

新框架应对不平衡回归挑战 · 2篇论文

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两篇在arXiv上发表的学术论文,提出了不平衡回归的新方法。
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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Vitor M. Leitao, Juscimara G. Avelino, George D. C. Cavalcanti, Rafael M. O. Cruz ·

    基于实例硬度的不平衡回归相关性

    arXiv:2607.20173v1 Announce Type: new Abstract: Imbalanced regression problems arise when the target variable has an asymmetric distribution, resulting in underrepresented value ranges in the dataset. Traditional approaches for identifying rare instances rely on a relevance funct…

  2. arXiv cs.AI TIER_1 English(EN) · Shermin Shahbazi, Hossein Mohammadi, Mohsen Afsharchi ·

    DADIR:一种密度感知的数据级不平衡回归框架

    arXiv:2607.17178v1 Announce Type: cross Abstract: Imbalanced learning addresses predictive modeling problems with underrepresented regions of the data distribution. Although widely studied in classification, imbalanced regression remains challenging because of continuous target v…