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One-Class Classification

One-class classification (OCC) algorithms serve a crucial role in scenarios where the negative class is either absent, poorly sampled, or not well defined. This unique situation presents a challenge for building effective classifiers, as they must delineate the class boundary solely based on knowledge of the positive class. OCC has found application in various research domains, including outlier/novelty detection and concept learning.

In the context of anomaly detection, OCC models are trained exclusively on "normal" data and are subsequently tasked with identifying anomalous patterns during inference.

A one-class classifier aims at capturing characteristics of training instances, in order to be able to distinguish between them and potential outliers to appear.

— Page 139, Learning from Imbalanced Data Sets, 2018.

Papers

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Showing 61–70 of 227 papers

TitleStatusHype
Learning Off-Road Terrain Traversability with Self-Supervisions Only—0
Wooden Sleeper Deterioration Detection for Rural Railway Prognostics Using Unsupervised Deeper FCDDs—0
SLSG: Industrial Image Anomaly Detection by Learning Better Feature Embeddings and One-Class Classification—0
Hyperspectral Image Analysis with Subspace Learning-based One-Class Classification—0
Harnessing Contrastive Learning and Neural Transformation for Time Series Anomaly Detection—0
PseudoBound: Limiting the anomaly reconstruction capability of one-class classifiers using pseudo anomalies—0
Diversity-Measurable Anomaly DetectionCode1
Synthetic Pseudo Anomalies for Unsupervised Video Anomaly Detection: A Simple yet Efficient Framework based on Masked Autoencoder—0
Unsupervised Deep One-Class Classification with Adaptive Threshold based on Training Dynamics—0
Anomaly Segmentation for High-Resolution Remote Sensing Images Based on Pixel DescriptorsCode1
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