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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 161–170 of 227 papers

TitleStatusHype
Detecting Adversarial Examples for Speech Recognition via Uncertainty QuantificationCode0
Evolutionary Simplicial Learning as a Generative and Compact Sparse Framework for Classification—0
Towards Anomaly Detection in Dashcam Videos—0
Robust Classification of High-Dimensional Spectroscopy Data Using Deep Learning and Data Synthesis—0
Ellipsoidal Subspace Support Vector Data DescriptionCode0
Meta-SVDD: Probabilistic Meta-Learning for One-Class Classification in Cancer Histology Images—0
DROCC: Deep Robust One-Class Classification—0
Boosting rare benthic macroinvertebrates taxa identification with one-class classification—0
Simple and Effective Prevention of Mode Collapse in Deep One-Class Classification—0
Comparison of Statistical and Machine Learning Techniques for Physical Layer Authentication—0
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