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Accurate Graph-Based PU Learning without Class Prior
Cited 1 time in
Web of Science
Cited 4 time in Scopus
- Authors
- Issue Date
- 2021-01
- Publisher
- IEEE
- Citation
- Proceedings - IEEE International Conference on Data Mining, ICDM, Vol.2021-December, pp.827-836
- Abstract
- © 2021 IEEE.How can we classify graph-structured data only with positive labels? Graph-based positive-unlabeled (PU) learning is to train a binary classifier given only the positive labels when the relationship between examples is given as a graph. The problem is of great importance for various tasks such as detecting malicious accounts in a social network, which are difficult to be modeled by supervised learning when the true negative labels are absent. Previous works for graph-based PU learning assume that the prior distribution of positive nodes is known in advance, which is not true in many real-world cases. In this work, we propose GRAB (Graph-based Risk minimization with iterAtive Belief propagation), a novel end-to-end approach for graph-based PU learning that requires no class prior. GRAB models a given graph as a Markov network and runs the marginalization and update steps iteratively. The marginalization step estimates the marginals of latent variables, while the update step trains a classifier network utilizing the computed priors in the objective function. Extensive experiments on five datasets show that GRAB achieves state-of-the-art accuracy, even compared with previous methods that are given the true prior.
- ISSN
- 1550-4786
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