ESA: Example Sieve Approach for Multi-Positive and Unlabeled Learning

Autor:	Li, Zhongnian, Wei, Meng, Ying, Peng, Xu, Xinzheng
Rok vydání:	2024
Předmět:	Computer Science - Machine Learning
Druh dokumentu:	Working Paper
DOI:	10.1145/3701551.3703526
Popis:	Learning from Multi-Positive and Unlabeled (MPU) data has gradually attracted significant attention from practical applications. Unfortunately, the risk of MPU also suffer from the shift of minimum risk, particularly when the models are very flexible as shown in Fig.\ref{moti}. In this paper, to alleviate the shifting of minimum risk problem, we propose an Example Sieve Approach (ESA) to select examples for training a multi-class classifier. Specifically, we sieve out some examples by utilizing the Certain Loss (CL) value of each example in the training stage and analyze the consistency of the proposed risk estimator. Besides, we show that the estimation error of proposed ESA obtains the optimal parametric convergence rate. Extensive experiments on various real-world datasets show the proposed approach outperforms previous methods. Comment: 12 pages, 6 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2412.02240 Zobrazit plný text záznamu View this record from Arxiv