Dens-PU: PU Learning with Density-Based Positive Labeled Augmentation
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866911936953712640 |
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| author | Sevetlidis, Vasileios Pavlidis, George Mouroutsos, Spyridon Gasteratos, Antonios |
| author_facet | Sevetlidis, Vasileios Pavlidis, George Mouroutsos, Spyridon Gasteratos, Antonios |
| contents | This study proposes a novel approach for solving the PU learning problem based on an anomaly-detection strategy. Latent encodings extracted from positive-labeled data are linearly combined to acquire new samples. These new samples are used as embeddings to increase the density of positive-labeled data and, thus, define a boundary that approximates the positive class. The further a sample is from the boundary the more it is considered as a negative sample. Once a set of negative samples is obtained, the PU learning problem reduces to binary classification. The approach, named Dens-PU due to its reliance on the density of positive-labeled data, was evaluated using benchmark image datasets, and state-of-the-art results were attained. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2303_11848 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Dens-PU: PU Learning with Density-Based Positive Labeled Augmentation Sevetlidis, Vasileios Pavlidis, George Mouroutsos, Spyridon Gasteratos, Antonios Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition This study proposes a novel approach for solving the PU learning problem based on an anomaly-detection strategy. Latent encodings extracted from positive-labeled data are linearly combined to acquire new samples. These new samples are used as embeddings to increase the density of positive-labeled data and, thus, define a boundary that approximates the positive class. The further a sample is from the boundary the more it is considered as a negative sample. Once a set of negative samples is obtained, the PU learning problem reduces to binary classification. The approach, named Dens-PU due to its reliance on the density of positive-labeled data, was evaluated using benchmark image datasets, and state-of-the-art results were attained. |
| title | Dens-PU: PU Learning with Density-Based Positive Labeled Augmentation |
| topic | Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2303.11848 |