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Table 2 Surveyed works on novelty detection and OCC

From: A literature review on one-class classification and its potential applications in big data

# Paper Method(s) Domain
1 Identification of patient deterioration in vital-sign data using one-class support vector machines [45] GMM, OCSVM Healthcare
2 One-class classification with Gaussian processes [46] Gaussian, SVDD Generic datasets
3 A one-class SVM based tool for machine learning novelty detection in HVAC chiller systems [47] PCA, OCSVM HVAC systems
4 Deep Gaussian Process autoencoders for novelty detection [48] DGP UCI datasets
5 Improving one class support vector machine novelty detection scheme using nonlinear features [49] OCSVM Vibration signals
6 Active learning-based Support Vector Data Description method for robust novelty detection [50] Active Learning based SVDD UCI datasets
7 Novelty detection using deep normative modeling for imu-based abnormal movement monitoring in Parkinson’s disease and Autism Spectrum Disorders [51] OCSVM Patient monitoring, healthcare
8 Adversarially learned one-class classifier for novelty detection [52, 54] OCC GAN, LOF, DRAE Image processing, video processing
9 From one-class to two-class classification by incorporating expert knowledge: Novelty detection in human behaviour [55] Expert-based Two-Class Classification Fraud detection
10 Robust AdaBoost based ensemble of one-class support vector machines [56] AdaBoost, OCSVM UCI datasets
11 OCGAN: One-class novelty detection using GANs with constrained latent representations [58] OCGAN Image processing, object recognition
12 Adversarially learned one-class novelty detection with confidence estimation [59] Adversarial OCC Image processing