HasLoss:一个新的 Hassanat 基于距离的损失函数用于二进制分类
1Faculty of Information Technology, Department of Data Science, Mutah University, Karak, Jordan.
Frontiers in artificial intelligence
|February 26, 2026
概括
新的基于距离的损失函数,适应了Hassanat距离用于二进制分类,为机器学习模型提供了强大的训练. 这些Hassanat损失提供了理论上的保证和竞争性表现,显示出对异常值和噪声的强度提高.
科学领域:
- 机器学习 机器学习
- 计算机科学 计算机科学
- 优化理论 优化理论
背景情况:
- 损失函数对于训练机器学习模型,特别是分类中的神经网络至关重要.
- 现有的损失函数可能缺乏对异常值和噪声的稳定性,影响模型性能.
研究的目的:
- 建立基于距离的损失函数的理论框架,使用Hassanat距离进行二进制分类.
- 开发和经验验证基于Hassanat的新损失函数变体.
主要方法:
- 调整了Hassanat距离用于二进制分类,以创建基于距离的损失函数的理论框架.
- 进行了梯度分析,以证明 Hassanat 损失的边界梯度和有限的利普希茨常数.
- 在合成数据集和9个现实数据集上经验评估了6个Hassanat损失变体,与二进制交叉 (BCE),焦点损失,平均平方误差 (MSE) 和L1损失进行比较.
主要成果:
- 基于Hassanat的损失显示出具有竞争力的性能,与可比或改进的校准,融合速度,精度,召回,F1得分和AUC.
- 拟议的损失显示出对异常值和噪声的显著稳定性.
- 科恩的数据分析表明,一些Hassanat变体的实际效果大小比BCE.
结论:
- 建立了基于距离的Hassanat损失函数的理论基础和经验验证.
- 有限梯度和有限的利普希茨常数提供了优化保证,并解释了观察到的稳定性.
- 该框架允许系统地开发针对特定应用的强大的损失函数.
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