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Keystroke Analysis as a Method of Advanced User Authentication and Response
SEC '02 Proceedings of the IFIP TC11 17th International Conference on Information Security: Visions and Perspectives
Combining One-Class Classifiers
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Tree Induction for Probability-Based Ranking
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Keystroke analysis of free text
ACM Transactions on Information and System Security (TISSEC)
Kernel Fisher Discriminants for Outlier Detection
Neural Computation
Outlier detection by active learning
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LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
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Role-based differentiation for insider detection algorithms
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Robotics and Autonomous Systems
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A new frontier in novelty detection: pattern recognition of stochastically episodic events
ACIIDS'11 Proceedings of the Third international conference on Intelligent information and database systems - Volume Part I
On simulating episodic events against a background of noise-like non-episodic events
Proceedings of the 2010 Summer Computer Simulation Conference
Anomaly detection using ensembles
MCS'11 Proceedings of the 10th international conference on Multiple classifier systems
Detection of text quality flaws as a one-class classification problem
Proceedings of the 20th ACM international conference on Information and knowledge management
RED'10 Proceedings of the Third international conference on Resource Discovery
Novelty detection in wildlife scenes through semantic context modelling
Pattern Recognition
Predicting quality flaws in user-generated content: the case of wikipedia
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Canadian AI'12 Proceedings of the 25th Canadian conference on Advances in Artificial Intelligence
Bayesian multiple imputation approaches for one-class classification
Canadian AI'12 Proceedings of the 25th Canadian conference on Advances in Artificial Intelligence
On the pattern recognition and classification of stochastically episodic events
Transactions on Compuational Collective Intelligence VI
Concurrency and Computation: Practice & Experience
Proceedings of the 21st ACM international conference on Information and knowledge management
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
Detecting insider threats in a real corporate database of computer usage activity
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Pattern Recognition
Review: A review of novelty detection
Signal Processing
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One-class classification has important applications such as outlier and novelty detection. It is commonly tackled using density estimation techniques or by adapting a standard classification algorithm to the problem of carving out a decision boundary that describes the location of the target data. In this paper we investigate a simple method for one-class classification that combines the application of a density estimator, used to form a reference distribution, with the induction of a standard model for class probability estimation. In this method, the reference distribution is used to generate artificial data that is employed to form a second, artificial class. In conjunction with the target class, this artificial class is the basis for a standard two-class learning problem. We explain how the density function of the reference distribution can be combined with the class probability estimates obtained in this way to form an adjusted estimate of the density function of the target class. Using UCI datasets, and data from a typist recognition problem, we show that the combined model, consisting of both a density estimator and a class probability estimator, can improve on using either component technique alone when used for one-class classification. We also compare the method to one-class classification using support vector machines.