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
Estimating the Support of a High-Dimensional Distribution
Neural Computation
One-class document classification via Neural Networks
Neurocomputing
Combatting financial fraud: a coevolutionary anomaly detection approach
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
A kernel autoassociator approach to pattern classification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Scaling Genetic Programming to Large Datasets Using Hierarchical Dynamic Subset Selection
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A parallel genetic programming for single class classification
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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One-class classification naturally only provides one-class of exemplars, the target class, from which to construct the classification model. The one-class approach is constructed from artificial data combined with the known in-class exemplars. A multi-objective fitness function in combination with a local membership function is then used to encourage a co-operative coevolutionary decomposition of the original problem under a novelty detection model of classification. Learners are therefore associated with different subsets of the target class data and encouraged to tradeoff detection versus false positive performance; where this is equivalent to assessing the misclassification of artificial exemplars versus detection of subsets of the target class. Finally, the architecture makes extensive use of active learning to reinforce the scalability of the overall approach.