June 7, 2024, 4:42 a.m. | Atsutoshi Kumagai, Tomoharu Iwata, Yasuhiro Fujiwara

cs.LG updates on arXiv.org arxiv.org

arXiv:2406.03680v1 Announce Type: new
Abstract: We propose a meta-learning method for positive and unlabeled (PU) classification, which improves the performance of binary classifiers obtained from only PU data in unseen target tasks. PU learning is an important problem since PU data naturally arise in real-world applications such as outlier detection and information retrieval. Existing PU learning methods require many PU data, but sufficient data are often unavailable in practice. The proposed method minimizes the test classification risk after the model …

abstract applications arxiv binary classification classifiers cs.lg data detection information meta meta-learning outlier performance positive problem retrieval stat.ml tasks type world

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