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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 141–150 of 227 papers

TitleStatusHype
Towards Anomaly Detection in Dashcam Videos—0
Towards Fair Deep Anomaly Detection—0
Towards Targeted Change Detection with Heterogeneous Remote Sensing Images for Forest Mortality Mapping—0
Unsupervised Transfer Learning for Anomaly Detection: Application to Complementary Operating Condition Transfer—0
Trustworthiness of X Users: A One-Class Classification Approach—0
Uncertainty-Based Out-of-Distribution Classification in Deep Reinforcement Learning—0
Understanding Time Series Anomaly State Detection through One-Class Classification—0
Unsupervised Artifact Detection for Whole Slide Images of Prostate Biopsies—0
Unsupervised Deep One-Class Classification with Adaptive Threshold based on Training Dynamics—0
Unsupervised Learning of the Set of Local Maxima—0
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