Positive-Unlabeled Classification under Class Prior Shift and Asymmetric Error

Abstract

Bottlenecks of binary classification from positive and unlabeled data (PU classification) are the requirements that given unlabeled patterns are drawn from the test marginal distribution, and the penalty of the false positive error is identical to the false negative error. However, such requirements are often not fulfilled in practice. In this paper, we generalize PU classification to the class prior shift and asymmetric error scenarios. Based on the analysis of the Bayes optimal classifier, we show that given a test class prior, PU classification under class prior shift is equivalent to PU classification with asymmetric error. Then, we propose two different frameworks to handle these problems, namely, a risk minimization framework and density ratio estimation framework. Finally, we demonstrate the effectiveness of the proposed frameworks and compare both frameworks through experiments using benchmark datasets.

Fixed typos

References in corpus (6)

• On the Convergence of Adam and Beyond
• PU Learning for Matrix Completion
• Mixture Proportion Estimation via Kernel Embedding of Distributions
• Theoretical Comparisons of Positive-Unlabeled Learning against Positive-Negative Learning
• On the Minimal Supervision for Training Any Binary Classifier from Only Unlabeled Data
• Trimmed Density Ratio Estimation