A probabilistic resource allocating network for novelty detection
Neural Computation
A note on comparing classifiers
Pattern Recognition Letters
Machine Learning
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Clustering through decision tree construction
Proceedings of the ninth international conference on Information and knowledge management
Outlier detection for high dimensional data
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Machine Learning
A Comparison of Ranking Methods for Classification Algorithm Selection
ECML '00 Proceedings of the 11th European Conference on Machine Learning
When Is ''Nearest Neighbor'' Meaningful?
ICDT '99 Proceedings of the 7th International Conference on Database Theory
Outlier Detection Using Classifier Instability
SSPR '98/SPR '98 Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
Ensemble Methods in Machine Learning
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Combining One-Class Classifiers
MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems
Inference for the Generalization Error
Machine Learning
One-class svms for document classification
The Journal of Machine Learning Research
Uniform object generation for optimizing one-class classifiers
The Journal of Machine Learning Research
Novelty detection: a review—part 1: statistical approaches
Signal Processing
Novelty detection: a review—part 2: neural network based approaches
Signal Processing
Support Vector Data Description
Machine Learning
Authorship verification as a one-class classification problem
ICML '04 Proceedings of the twenty-first international conference on Machine learning
A Survey of Outlier Detection Methodologies
Artificial Intelligence Review
Using artificial anomalies to detect unknown and known network intrusions
Knowledge and Information Systems
Estimating the Support of a High-Dimensional Distribution
Neural Computation
Machine Learning
Outlier detection by active learning
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
A Comparison of Decision Tree Ensemble Creation Techniques
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Adaptive, nonparametric markov models and information-theoretic methods for image restoration and segmentation
On the Choice of Smoothing Parameters for Parzen Estimators of Probability Density Functions
IEEE Transactions on Computers
Mining Supervised Classification Performance Studies: A Meta-Analytic Investigation
Journal of Classification
One-Class Classification by Combining Density and Class Probability Estimation
ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
An evaluation of one-class classification techniques for speaker verification
Artificial Intelligence Review
ACM Computing Surveys (CSUR)
A generalized Shapiro-Wilk W statistic for testing high-dimensional normality
Computational Statistics & Data Analysis
Influence of Hyperparameters on Random Forest Accuracy
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
Ensembles of One Class Support Vector Machines
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
A novelty detection approach to classification
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
An experimental study on rotation forest ensembles
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
A comparison of one-class classifiers for novelty detection in forensic case data
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
A survey of recent trends in one class classification
AICS'09 Proceedings of the 20th Irish conference on Artificial intelligence and cognitive science
Novelty detection in projected spaces for structural health monitoring
IDA'10 Proceedings of the 9th international conference on Advances in Intelligent Data Analysis
Nonparametric multivariate density estimation: a comparative study
IEEE Transactions on Signal Processing
Cluster-based probability model and its application to image and texture processing
IEEE Transactions on Image Processing
A new random forest method for one-class classification
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
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One class classification is a binary classification task for which only one class of samples is available for learning. In some preliminary works, we have proposed One Class Random Forests (OCRF), a method based on a random forest algorithm and an original outlier generation procedure that makes use of classifier ensemble randomization principles. In this paper, we propose an extensive study of the behavior of OCRF, that includes experiments on various UCI public datasets and comparison to reference one class namely, Gaussian density models, Parzen estimators, Gaussian mixture models and One Class SVMs-with statistical significance. Our aim is to show that the randomization principles embedded in a random forest algorithm make the outlier generation process more efficient, and allow in particular to break the curse of dimensionality. One Class Random Forests are shown to perform well in comparison to other methods, and in particular to maintain stable performance in higher dimension, while the other algorithms may fail.