Learning from Positive and Unlabeled Data with Augmented Classes

• URL: http://arxiv.org/abs/2207.13274v1
• Date: Wed, 27 Jul 2022 03:40:50 GMT
• Title: Learning from Positive and Unlabeled Data with Augmented Classes
• Authors: Zhongnian Li, Liutao Yang, Zhongchen Ma, Tongfeng Sun, Xinzheng Xu and Daoqiang Zhang
• Abstract summary: We propose an unbiased risk estimator for PU learning with Augmented Classes (PUAC) We derive the estimation error bound for the proposed estimator, which provides a theoretical guarantee for its convergence to the optimal solution.
• Score: 17.97372291914351
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Positive Unlabeled (PU) learning aims to learn a binary classifier from only positive and unlabeled data, which is utilized in many real-world scenarios. However, existing PU learning algorithms cannot deal with the real-world challenge in an open and changing scenario, where examples from unobserved augmented classes may emerge in the testing phase. In this paper, we propose an unbiased risk estimator for PU learning with Augmented Classes (PUAC) by utilizing unlabeled data from the augmented classes distribution, which can be easily collected in many real-world scenarios. Besides, we derive the estimation error bound for the proposed estimator, which provides a theoretical guarantee for its convergence to the optimal solution. Experiments on multiple realistic datasets demonstrate the effectiveness of proposed approach.

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