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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 151–160 of 227 papers

TitleStatusHype
Learning representations for multivariate time series with missing data using Temporal Kernelized Autoencoders—0
LLM meets Vision-Language Models for Zero-Shot One-Class Classification—0
LOSDD: Leave-Out Support Vector Data Description for Outlier Detection—0
Lp-Norm Constrained One-Class Classifier Combination—0
Medical Knowledge-Guided Deep Learning for Imbalanced Medical Image Classification—0
Meta-learning One-class Classifiers with Eigenvalue Solvers for Supervised Anomaly Detection—0
Meta-SVDD: Probabilistic Meta-Learning for One-Class Classification in Cancer Histology Images—0
Minimum Variance Embedded Auto-associative Kernel Extreme Learning Machine for One-class Classification—0
Mobile authentication of copy detection patterns—0
Modeling and Recognition of Smart Grid Faults by a Combined Approach of Dissimilarity Learning and One-Class Classification—0
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