| Autor: |
MohammadHossein Reshadi, Wen Li, Wenjie Xu, Precious Omashor, Albert Dinh, Scott Dick, Yuntong She, Michael Lipsett |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Algorithms, Vol 17, Iss 3, p 114 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
1999-4893 |
| DOI: |
10.3390/a17030114 |
| Popis: |
Anomaly detection in data streams (and particularly time series) is today a vitally important task. Machine learning algorithms are a common design for achieving this goal. In particular, deep learning has, in the last decade, proven to be substantially more accurate than shallow learning in a wide variety of machine learning problems, and deep anomaly detection is very effective for point anomalies. However, deep semi-supervised contextual anomaly detection (in which anomalies within a time series are rare and none at all occur in the algorithm’s training data) is a more difficult problem. Hybrid anomaly detectors (a “normal model” followed by a comparator) are one approach to these problems, but the separate loss functions for the two components can lead to inferior performance. We investigate a novel synthetic-example oversampling technique to harmonize the two components of a hybrid system, thus improving the anomaly detector’s performance. We evaluate our algorithm on two distinct problems: identifying pipeline leaks and patient-ventilator asynchrony. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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