Beyond the Selected Completely at Random Assumption for Learning from Positive and Unlabeled Data-Reference-Cited by-同舟云学术

Beyond the Selected Completely at Random Assumption for Learning from Positive and Unlabeled Data

Published:2020 Issue: Volume: Page:71-85
ISSN:0302-9743
Container-title:Machine Learning and Knowledge Discovery in Databases
language:
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Author:

Bekker Jessa^ORCID,Robberechts Pieter^ORCID,Davis Jesse^ORCID

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-030-46147-8_5

Reference37 articles.

1. Bekker, J., Davis, J.: Estimating the class prior in positive and unlabeled data through decision tree induction. In: Proceedings of the 32th AAAI Conference on Artificial Intelligence (2018)

2. Bekker, J., Davis, J.: Learning from positive and unlabeled data: a survey. In: Machine Learning (2020)

3. Blanchard, G., Lee, G., Scott, C.: Semi-supervised novelty detection. J. Mach. Learn. Res. 11, 2973–3009 (2010)

4. Blockeel, H.: PU-learning disjunctive concepts in ILP. In: ILP 2017 Late Breaking Papers (2017)

5. Chang, S., et al.: Positive-unlabeled learning in streaming networks. In: Proceedings of the 22nd ACM Conference on Knowledge Discovery and Data Mining, pp. 755–764 (2016)

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