直接优化精确率与召回率，解决不平衡分类难题
Exact Reformulation and Optimization for Direct Metric Optimization in Binary Imbalanced Classification
- 提出精确约束重构方法，避免使用平滑近似
- 在多个数据集上优于现有最先进方法
- 适合对特定指标有严格要求的场景
在类别不平衡的分类任务中，标准准确率会误导评估结果。现有方法多优化平衡准确率（即各类别召回率的平均值），但在类别重要性不同或需达到指定指标时表现不足。本文研究精确率和召回率在三种典型二分类不平衡场景下的直接指标优化：固定精确率优化召回率（FPOR）、固定召回率优化精确率（FROP）以及优化F1分数（OFOS）。不同于以往依赖光滑近似处理指示函数的方法，本文首次提出这些直接指标优化问题的精确约束重构形式，并可通过精确罚方法有效求解。在多个基准数据集上的实验表明，该方法在三类直接指标优化问题上均显著优于现有最先进技术。我们预期所提出的精确重构与优化（ERO）框架可推广至更广泛的二分类不平衡场景及其他指标优化问题。代码已开源：https://github.com/sun-umn/DMO。
原文摘要 · Abstract (English)
For classification with imbalanced class frequencies, i.e., imbalanced classification (IC), standard accuracy is known to be misleading as a performance measure. While most existing methods for IC resort to optimizing balanced accuracy (i.e., the average of class-wise recalls), they fall short in scenarios where the significance of classes varies or certain metrics should reach prescribed levels. In this paper, we study two key classification metrics, precision and recall, under three practical binary IC settings: fix precision optimize recall (FPOR), fix recall optimize precision (FROP), and optimize $F_1$-score (OFOS). Unlike existing methods that rely on smooth approximations to deal with the indicator function involved, we introduce, for the first time, exact constrained reformulations for these direct metric optimization (DMO) problems, which can be effectively solved by exact penalty methods. Experiment results on multiple benchmark datasets demonstrate the practical superiority of our approach over the state-of-the-art methods for the three DMO problems. We also expect our exact reformulation and optimization (ERO) framework to be applicable to a wide range of DMO problems for binary IC and beyond. Our code is available at https://github.com/sun-umn/DMO.
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