Averaging regularized estimators
Neural Computation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Support vector domain description
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Concept learning in the absence of counterexamples: an autoassociation-based approach to classification
A Discussion on the Classifier Projection Space for Classifier Combining
MCS '02 Proceedings of the Third International Workshop on Multiple Classifier Systems
Multi-resolution subspace for financial trading
Pattern Recognition Letters
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
Stacking for Ensembles of Local Experts in Metabonomic Applications
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
Recognition of Properties by Probabilistic Neural Networks
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
Language Resources and Evaluation
Complexity and multithreaded implementation analysis of one class-classifiers fuzzy combiner
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part II
Pruned random subspace method for one-class classifiers
MCS'11 Proceedings of the 10th international conference on Multiple classifier systems
DAS'06 Proceedings of the 7th international conference on Document Analysis Systems
Combining diverse one-class classifiers
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part II
Cluster-based one-class ensemble for classification problems in information retrieval
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Video synchronization as one-class learning
Proceedings of the 27th Conference on Image and Vision Computing New Zealand
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
Combining one-class classifiers via meta learning
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Pattern Recognition
ACTIDS: an active strategy for detecting and localizing network attacks
Proceedings of the 2013 ACM workshop on Artificial intelligence and security
A survey of multiple classifier systems as hybrid systems
Information Fusion
Clustering-based ensembles for one-class classification
Information Sciences: an International Journal
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In the problem of one-class classification target objects should be distinguished from outlier objects. In this problem it is assumed that only information of the target class is available while nothing is known about the outlier class. Like standard two-class classifiers, one-class classifiers hardly ever fit the data distribution perfectly. Using only the best classifier and discarding the classifiers with poorer performance might waste valuable information. To improve performance the results of different classifiers (which may differ in complexity or training algorithm) can be combined. This can not only increase the performance but it can also increase the robustness of the classification. Because for one-class classifiers only information of one of the classes is present, combining one-class classifiers is more difficult. In this paper we investigate if and how one-class classifiers can be combined best in a handwritten digit recognition problem.