Neural Networks - Special issue: automatic target recognition
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Sum Versus Vote Fusion in Multiple Classifier Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combining One-Class Classifiers
MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems
An Overview and Comparison of Voting Methods for Pattern Recognition
IWFHR '02 Proceedings of the Eighth International Workshop on Frontiers in Handwriting Recognition (IWFHR'02)
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
A Survey of Outlier Detection Methodologies
Artificial Intelligence Review
Two-stage binary classifier with fuzzy-valued loss function
Pattern Analysis & Applications
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
Multi-category classification by soft-max combination of binary classifiers
MCS'03 Proceedings of the 4th international conference on Multiple classifier systems
Pattern Classification Using Ensemble Methods
Pattern Classification Using Ensemble Methods
Costs-sensitive classification in multistage classifier with fuzzy observations of object features
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part II
New results on error correcting output codes of kernel machines
IEEE Transactions on Neural Networks
Oversampling methods for classification of imbalanced breast cancer malignancy data
ICCVG'12 Proceedings of the 2012 international conference on Computer Vision and Graphics
Diversity measures for one-class classifier ensembles
Neurocomputing
Cost-sensitive decision tree ensembles for effective imbalanced classification
Applied Soft Computing
Clustering-based ensembles for one-class classification
Information Sciences: an International Journal
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Multiple Classifier Systems (MCSs) are the focus of intense research and a large variety of methods have been developed in order to exploit strengths of individual classifiers. In this paper we address the problem how to implement a multi-class classifier by an ensemble of one-class classifiers. To improve the performance of a compound classifier, different individual classifiers (which may e.g., differ in complexity, type, training algorithm or other) can be combined and that could increase its both performance, and robustness. The model of one-class classifiers is dedicated to recognize one class only, therefore it is a quite difficult to produce MCSs on the basis of it. One of the important problem is how to ensure diversity of classifier ensemble which consists of one-class classifiers. Well-known diversity measures have been developed for committees of multiclass classifiers. In this work we propose a novel diversity measure which can be applied to a set of one-class classifiers. Additionally we propose a classifier fusion model dedicated to one-class classifiers, which allows more than one classifier per class. We will try answer the question if increasing number of individual one-class classifier has an impact on quality of MCS. The proposed model was evaluated by computer experiments and their results prove that proposed model can outperform well known fusion methods.