The Deep Radial Basis Function Data Descriptor (D-RBFDD) Network: A One-Class Neural Network for Anomaly Detection
نویسندگان
چکیده
Anomaly detection is a challenging problem in machine learning, and made even more so when dealing with instances that are captured low-level, raw data representations without well-known well-behaved set of engineered features. Images or streams from sensors good examples such representations. The Radial Basis Function Data Descriptor (RBFDD) network an effective solution for anomaly detection, however, it shallow model does not deal effectively This paper investigates approaches to improving the RBFDD transform into deep one-class classifier suitable problems We show based on simple transfer learning our results suggest this because latent learned by generic classification models detection. Instead, we approach adds multiple convolutional layers before (RBF) layer network—to form Deep (D-RBFDD) network—is very effective. shown evaluation experiments using scenarios created publicly available image datasets, real-world dataset which different types arrhythmia detected electrocardiogram (ECG) data. Our D-RBFDD out-performs state-of-the-art methods including Support Vector (Deep SVDD), One-Class Machine (OCSVM), Isolation Forest produces competitive ECG dataset.
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ژورنال
عنوان ژورنال: IEEE Access
سال: 2022
ISSN: ['2169-3536']
DOI: https://doi.org/10.1109/access.2022.3187961