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 |