SelectNet: Learning to Sample from the Wild for Imbalanced Data Training Article Swipe
Yunru Liu
,
Tingran Gao
,
Haizhao Yang
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1905.09872
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1905.09872
Supervised learning from training data with imbalanced class sizes, a commonly encountered scenario in real applications such as anomaly/fraud detection, has long been considered a significant challenge in machine learning. Motivated by recent progress in curriculum and self-paced learning, we propose to adopt a semi-supervised learning paradigm by training a deep neural network, referred to as SelectNet, to selectively add unlabelled data together with their predicted labels to the training dataset. Unlike existing techniques designed to tackle the difficulty in dealing with class imbalanced training data such as resampling, cost-sensitive learning, and margin-based learning, SelectNet provides an end-to-end approach for learning from important unlabelled data "in the wild" that most likely belong to the under-sampled classes in the training data, thus gradually mitigates the imbalance in the data used for training the classifier. We demonstrate the efficacy of SelectNet through extensive numerical experiments on standard datasets in computer vision.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/1905.09872
- https://arxiv.org/pdf/1905.09872
- OA Status
- green
- Cited By
- 3
- References
- 24
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2945558214
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- OpenAlex ID
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https://openalex.org/W2945558214Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.1905.09872Digital Object Identifier
- Title
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SelectNet: Learning to Sample from the Wild for Imbalanced Data TrainingWork title
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preprintOpenAlex work type
- Language
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enPrimary language
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2019Year of publication
- Publication date
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2019-05-23Full publication date if available
- Authors
-
Yunru Liu, Tingran Gao, Haizhao YangList of authors in order
- Landing page
-
https://arxiv.org/abs/1905.09872Publisher landing page
- PDF URL
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https://arxiv.org/pdf/1905.09872Direct link to full text PDF
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/1905.09872Direct OA link when available
- Concepts
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Machine learning, Artificial intelligence, Computer science, Resampling, Margin (machine learning), Artificial neural network, Classifier (UML), Anomaly detection, Training set, Semi-supervised learning, Training (meteorology), Supervised learning, Labeled data, Deep learning, Physics, MeteorologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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3Total citation count in OpenAlex
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2022: 2, 2021: 1Per-year citation counts (last 5 years)
- References (count)
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24Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.detection, | 19 |
| abstract_inverted_index.difficulty | 78 |
| abstract_inverted_index.end-to-end | 97 |
| abstract_inverted_index.imbalanced | 6, 83 |
| abstract_inverted_index.self-paced | 37 |
| abstract_inverted_index.techniques | 73 |
| abstract_inverted_index.unlabelled | 60, 103 |
| abstract_inverted_index.classifier. | 132 |
| abstract_inverted_index.demonstrate | 134 |
| abstract_inverted_index.encountered | 11 |
| abstract_inverted_index.experiments | 142 |
| abstract_inverted_index.resampling, | 88 |
| abstract_inverted_index.selectively | 58 |
| abstract_inverted_index.significant | 25 |
| abstract_inverted_index.applications | 15 |
| abstract_inverted_index.margin-based | 92 |
| abstract_inverted_index.anomaly/fraud | 18 |
| abstract_inverted_index.under-sampled | 114 |
| abstract_inverted_index.cost-sensitive | 89 |
| abstract_inverted_index.semi-supervised | 44 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 3 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/16 |
| sustainable_development_goals[0].score | 0.6200000047683716 |
| sustainable_development_goals[0].display_name | Peace, Justice and strong institutions |
| citation_normalized_percentile |