通过自适应内核密度估计实现成本意识的AUC优化

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Peisong Wen, Qianqian Xu, Zhiyong Yang

IEEE transactions on pattern analysis and machine intelligence

|December 4, 2025

概括

本研究介绍了用于模型评估的成本意识的AUC (CAUC),适应非参数错误分类成本. 新的框架优化CAUC使用凸放松和自适应内核密度估计,以提高跨多种数据集的性能.

科学领域:

机器学习机器学习
统计建模统计建模
预测分析 (Predictive Analytics) 是一种分析方法.

背景情况:

ROC曲线下的面积 (AUC) 是模型性能评估的标准度量.
现有的AUC优化方法通常假定参数值分布,这可能不反映现实世界的非参数性成本分布.
最佳决策门受到错误分类成本的影响,需要采取成本意识的方法.

研究的目的:

引入成本意识的AUC (CAUC) 来考虑非参数性成本分布.
解决CAUC中固有的双级优化挑战.
开发一个强大的和理论上健全的CAUC优化框架.

主要方法:

应用凸起式放松,将非凸起式内部优化问题转化为凸起式问题.
开发了一个自适应的内核密度估计框架来处理假阳性率 (FPR) 的导数.
使用基于有限差异的随机算法进行模型优化,避免手动聚合函数设计.

主要成果:

拟议的算法实现了一个理论的收率为O(ε−4).
经验研究证明了CAUC框架在各种数据集中的有效性和稳定性.
这些方法成功地解决了CAUC优化中的非凸度和导数估计挑战.

结论:

开发的框架为优化成本意识的AUC提供了有效的解决方案.
该方法是稳固的,可以适应不同的数据集和成本分布.
这项工作通过结合非参数错误分类成本来推进模型评估.

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