提出新型随机算法，高效优化不平衡数据分类的AUC指标。
Stochastic Primal-Dual Double Block-Coordinate for Two-way Partial AUC Maximization
- 采用随机双块坐标更新原问题与对偶变量，适应凸与非凸场景。
- 理论证明收敛速度优于现有方法，实验显示更快收敛与更好泛化性能。
- 适合处理数据不平衡的高精度分类任务，如医疗诊断、欺诈检测。
两向局部AUC（TPAUC）是不平衡数据二分类中的关键评估指标，聚焦于特定真阳性率（TPR）和假阳性率（FPR）范围。然而，针对TPAUC优化的随机算法研究仍不充分，现有方法或局限于近似损失函数，或存在次优复杂度。为此，本文提出两种创新的随机原始-对偶双块坐标算法，用于最大化TPAUC。算法对原始变量和对偶变量均采用随机块坐标更新，适用于凸与非凸情形。我们提供了理论收敛速率分析，证明其显著优于先前方法。基于多个基准数据集的实验结果验证了算法优越性，展现出更快的收敛速度和更好的泛化能力。本工作推进了TPAUC优化的前沿技术，为实际机器学习应用提供了有效工具。
原文摘要 · Abstract (English)
Two-way partial AUC (TPAUC) is a critical performance metric for binary classification with imbalanced data, as it focuses on specific ranges of the true positive rate (TPR) and false positive rate (FPR). However, stochastic algorithms for TPAUC optimization remain under-explored, with existing methods either limited to approximated TPAUC loss functions or burdened by sub-optimal complexities. To overcome these limitations, we introduce two innovative stochastic primal-dual double block-coordinate algorithms for TPAUC maximization. These algorithms utilize stochastic block-coordinate updates for both the primal and dual variables, catering to both convex and non-convex settings. We provide theoretical convergence rate analyses, demonstrating significant improvements over prior approaches. Our experimental results, based on multiple benchmark datasets, validate the superior performance of our algorithms, showcasing faster convergence and better generalization. This work advances the state of the art in TPAUC optimization and offers practical tools for real-world machine learning applications.
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。