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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