Deep Transductive Outlier Detection
Fuente:
arXiv
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866916926231412736 |
|---|---|
| author | Klüttermann, Simon Müller, Emmanuel |
| author_facet | Klüttermann, Simon Müller, Emmanuel |
| contents | Outlier detection (OD) is one of the core challenges in machine learning. Transductive learning, which leverages test data during training, has shown promise in related machine learning tasks, yet remains largely unexplored for modern OD. We present Doust, the first end-to-end transductive deep learning algorithm for outlier detection, which explicitly leverages unlabeled test data to boost accuracy. On the comprehensive ADBench benchmark, Doust achieves an average ROC-AUC of $89%$, outperforming all 21 competitors by roughly $10%$. Our analysis identifies both the potential and a limitation of transductive OD: while performance gains can be substantial in favorable conditions, very low contamination rates can hinder improvements unless the dataset is sufficiently large. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_03495 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Deep Transductive Outlier Detection Klüttermann, Simon Müller, Emmanuel Machine Learning Outlier detection (OD) is one of the core challenges in machine learning. Transductive learning, which leverages test data during training, has shown promise in related machine learning tasks, yet remains largely unexplored for modern OD. We present Doust, the first end-to-end transductive deep learning algorithm for outlier detection, which explicitly leverages unlabeled test data to boost accuracy. On the comprehensive ADBench benchmark, Doust achieves an average ROC-AUC of $89%$, outperforming all 21 competitors by roughly $10%$. Our analysis identifies both the potential and a limitation of transductive OD: while performance gains can be substantial in favorable conditions, very low contamination rates can hinder improvements unless the dataset is sufficiently large. |
| title | Deep Transductive Outlier Detection |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2404.03495 |