| Autor: |
Vasileios Sevetlidis, George Pavlidis, Spyridon G. Mouroutsos, Antonios Gasteratos |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
IEEE Access, Vol 12, Pp 90287-90298 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2169-3536 |
| DOI: |
10.1109/ACCESS.2024.3420453 |
| Popis: |
In this study, a novel approach for solving the PU learning problem is proposed based on an anomaly detection strategy. A Convolutional Autoencoder (CAE) is used to extract latent encodings from positive-labeled data, which are then linearly combined to acquire new samples that lie between them. These new samples were used as embeddings to define a boundary that approximates the positive class. Data points that were significantly different from the majority of the data were assumed to be negative samples. Once a set of negative samples is obtained, the problem can be treated as a typical binary-classification problem. This approach was evaluated using benchmark image datasets, CIFAR-10 and Fashion-MNIST, yielding F1-scores of 91.96% and 94.80% on the two datasets respectively. These results demonstrate the efficacy of Dense-PU in enhancing classification performance in identifying negative samples in unlabeled data. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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