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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 101–110 of 227 papers

TitleStatusHype
Towards Targeted Change Detection with Heterogeneous Remote Sensing Images for Forest Mortality Mapping—0
Data refinement for fully unsupervised visual inspection using pre-trained networks—0
On The Effectiveness of One-Class Support Vector Machine in Different Defect Prediction Scenarios—0
SAFE-OCC: A Novelty Detection Framework for Convolutional Neural Network Sensors and its Application in Process Control—0
FROB: Few-shot ROBust Model for Classification and Out-of-Distribution Detection—0
Constrained Deep One-Class Feature Learning For Classifying Imbalanced Medical Images—0
Medical Knowledge-Guided Deep Learning for Imbalanced Medical Image Classification—0
Self-Supervised Predictive Convolutional Attentive Block for Anomaly DetectionCode1
UBnormal: New Benchmark for Supervised Open-Set Video Anomaly DetectionCode1
Conical Classification For Efficient One-Class Topic Determination—0
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