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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 61–70 of 227 papers

TitleStatusHype
Domain Adaptive Attention Learning for Unsupervised Person Re-Identification—0
DROCC: Deep Robust One-Class Classification—0
Convolutional autoencoder-based multimodal one-class classification—0
An Upper Bound for the Distribution Overlap Index and Its Applications—0
Evolutionary Simplicial Learning as a Generative and Compact Sparse Framework for Classification—0
Contextual One-Class Classification in Data Streams—0
Constrained Deep One-Class Feature Learning For Classifying Imbalanced Medical Images—0
Conical Classification For Efficient One-Class Topic Determination—0
Conical Classification For Computationally Efficient One-Class Topic Determination—0
Anomaly detection with semi-supervised classification based on risk estimators—0
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