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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
Simple and Effective Prevention of Mode Collapse in Deep One-Class Classification—0
SLSG: Industrial Image Anomaly Detection by Learning Better Feature Embeddings and One-Class Classification—0
Statistical and Machine Learning-based Decision Techniques for Physical Layer Authentication—0
STEP-GAN: A Step-by-Step Training for Multi Generator GANs with application to Cyber Security in Power Systems—0
Support Spinor Machine—0
Synthetic Pseudo Anomalies for Unsupervised Video Anomaly Detection: A Simple yet Efficient Framework based on Masked Autoencoder—0
Task-Specific Gradient Adaptation for Few-Shot One-Class Classification—0
Teacher Encoder-Student Decoder Denoising Guided Segmentation Network for Anomaly Detection—0
Timeseries Anomaly Detection using Temporal Hierarchical One-Class Network—0
Harnessing Contrastive Learning and Neural Transformation for Time Series Anomaly Detection—0
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