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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
Trustworthiness of X Users: A One-Class Classification Approach—0
Interleaving One-Class and Weakly-Supervised Models with Adaptive Thresholding for Unsupervised Video Anomaly DetectionCode1
Lp-Norm Constrained One-Class Classifier Combination—0
Advancing Image Retrieval with Few-Shot Learning and Relevance FeedbackCode0
Label-Free Multivariate Time Series Anomaly DetectionCode1
Learning Polynomial Representations of Physical Objects with Application to Certifying Correct Packing Configurations—0
OCGEC: One-class Graph Embedding Classification for DNN Backdoor DetectionCode0
Video Anomaly Detection via Spatio-Temporal Pseudo-Anomaly Generation : A Unified Approach—0
Enhancing Sentiment Analysis Results through Outlier Detection Optimization—0
Interpretable pap smear cell representation for cervical cancer screening—0
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