Robust AUC maximization for classification with pairwise confidence comparisons  

作　　者：Haochen SHI Mingkun XIE Shengjun HUANG 

机构地区：[1]College of Computer Science and Technology,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,China [2]College of Electronic and Information Engineering,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,China [3]MIIT Key Laboratory of Pattern Analysis and Machine Intelligence,Nanjing 211106,China

出　　处：《Frontiers of Computer Science》2024年第4期73-83,共11页中国计算机科学前沿（英文版）

基　　金：Natural Science Foundation of Jiangsu Province,China(BK20222012,BK20211517);National Key R&D Program of China(2020AAA0107000);National Natural Science Foundation of China(Grant No.62222605)。

摘　　要：Supervised learning often requires a large number of labeled examples,which has become a critical bottleneck in the case that manual annotating the class labels is costly.To mitigate this issue,a new framework called pairwise comparison(Pcomp)classification is proposed to allow training examples only weakly annotated with pairwise comparison,i.e.,which one of two examples is more likely to be positive.The previous study solves Pcomp problems by minimizing the classification error,which may lead to less robust model due to its sensitivity to class distribution.In this paper,we propose a robust learning framework for Pcomp data along with a pairwise surrogate loss called Pcomp-AUC.It provides an unbiased estimator to equivalently maximize AUC without accessing the precise class labels.Theoretically,we prove the consistency with respect to AUC and further provide the estimation error bound for the proposed method.Empirical studies on multiple datasets validate the effectiveness of the proposed method.

关 键 词：method pairwise WEAKLY 

分 类 号：O17[理学—数学]