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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 131–140 of 227 papers

TitleStatusHype
Deep One-Class Classification via Interpolated Gaussian DescriptorCode1
A Joint Representation Learning and Feature Modeling Approach for One-class Recognition—0
One-Class Classification: A Survey—0
One-class Classification Robust to Geometric Transformation—0
Towards Fair Deep Anomaly Detection—0
MOCCA: Multi-Layer One-Class ClassificAtion for Anomaly DetectionCode1
Timeseries Anomaly Detection using Temporal Hierarchical One-Class Network—0
FROCC: Fast Random projection-based One-Class Classification—0
Minimum Variance Embedded Auto-associative Kernel Extreme Learning Machine for One-class Classification—0
Learning and Evaluating Representations for Deep One-class ClassificationCode1
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