Related references
Note: Only part of the references are listed.Semi-supervised anomaly detection algorithms: A comparative summary and future research directions
Miryam Elizabeth Villa-Perez et al.
KNOWLEDGE-BASED SYSTEMS (2021)
A Unifying Review of Deep and Shallow Anomaly Detection
Lukas Ruff et al.
PROCEEDINGS OF THE IEEE (2021)
Bagging-RandomMiner: a one-class classifier for file access-based masquerade detection
Jose Benito Camina et al.
MACHINE VISION AND APPLICATIONS (2019)
Clustering-based proxy measure for optimizing one-class classifiers
Jaehong Yu et al.
PATTERN RECOGNITION LETTERS (2019)
Efficient Unsupervised Parameter Estimation for One-Class Support Vector Machines
Zahra Ghafoori et al.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2018)
An up-to-date comparison of state-of-the-art classification algorithms
Chongsheng Zhang et al.
EXPERT SYSTEMS WITH APPLICATIONS (2017)
Online personal risk detection based on behavioural and physiological patterns
Ari Yair Barrera-Animas et al.
INFORMATION SCIENCES (2017)
Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery
Thomas Schlegl et al.
INFORMATION PROCESSING IN MEDICAL IMAGING (IPMI 2017) (2017)
Fast structural ensemble for One-Class Classification
Jiachen Liu et al.
PATTERN RECOGNITION LETTERS (2016)
Ensemble of One-Class Classifiers for Personal Risk Detection Based on Wearable Sensor Data
Jorge Rodriguez et al.
SENSORS (2016)
Multi-class classification via heterogeneous ensemble of one-class classifiers
Seokho Kang et al.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE (2015)
One-class classifier ensemble pruning and weighting with firefly algorithm
Bartosz Krawczyk
NEUROCOMPUTING (2015)
On the usefulness of one-class classifier ensembles for decomposition of multi-class problems
Bartosz Krawczyk et al.
PATTERN RECOGNITION (2015)
The Windows-Users and -Intruder simulations Logs dataset (WUIL): An experimental framework for masquerade detection mechanisms
J. Benito Camina et al.
EXPERT SYSTEMS WITH APPLICATIONS (2014)
Clustering-based ensembles for one-class classification
Bartosz Krawczyk et al.
INFORMATION SCIENCES (2014)
Effects of data set features on the performances of classification algorithms
Ohbyung Kwon et al.
EXPERT SYSTEMS WITH APPLICATIONS (2013)
One class random forests
Chesner Desir et al.
PATTERN RECOGNITION (2013)
Intrusion detection in computer networks by a modular ensemble of one-class classifiers
Giorgio Giacinto et al.
INFORMATION FUSION (2008)
Growing a multi-class classifier with a reject option
D. M. J. Tax et al.
PATTERN RECOGNITION LETTERS (2008)
Top 10 algorithms in data mining
Xindong Wu et al.
KNOWLEDGE AND INFORMATION SYSTEMS (2008)
Reducing the dimensionality of data with neural networks
G. E. Hinton et al.
SCIENCE (2006)
Using artificial anomalies to detect unknown and known network intrusions
W Fan et al.
KNOWLEDGE AND INFORMATION SYSTEMS (2004)
Support vector data description
DMJ Tax et al.
MACHINE LEARNING (2004)
Estimating the support of a high-dimensional distribution
B Schölkopf et al.
NEURAL COMPUTATION (2001)
LOF: Identifying density-based local outliers
MM Breunig et al.
SIGMOD RECORD (2000)
Share Paper
