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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 31–40 of 227 papers

TitleStatusHype
A Coarse-to-Fine Pseudo-Labeling (C2FPL) Framework for Unsupervised Video Anomaly DetectionCode1
Viral Pneumonia Screening on Chest X-ray Images Using Confidence-Aware Anomaly DetectionCode1
Binary Classification from Positive-Confidence DataCode0
Learning Deep Features for One-Class ClassificationCode0
LBL: Logarithmic Barrier Loss Function for One-class ClassificationCode0
Improving State-of-the-Art in One-Class Classification by Leveraging Unlabeled DataCode0
Active Authentication using an Autoencoder regularized CNN-based One-Class ClassifierCode0
Identification of Abnormal States in Videos of Ants Undergoing Social Phase ChangeCode0
Adversarial Subspace Generation for Outlier Detection in High-Dimensional DataCode0
Impact of Channel Variation on One-Class Learning for Spoof DetectionCode0
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