Image segmentation with a hybrid ensemble of one-class support vector machines
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part I
Data with shifting concept classification using simulated recurrence
ACIIDS'12 Proceedings of the 4th Asian conference on Intelligent Information and Database Systems - Volume Part I
Ensemble of tensor classifiers based on the higher-order singular value decomposition
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part II
Cost-Sensitive splitting and selection method for medical decision support system
IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
CISIM'12 Proceedings of the 11th IFIP TC 8 international conference on Computer Information Systems and Industrial Management
Adaptive splitting and selection algorithm for classification of breast cytology images
ICCCI'12 Proceedings of the 4th international conference on Computational Collective Intelligence: technologies and applications - Volume Part I
Adaptive splitting and selection method for noninvasive recognition of liver fibrosis stage
ACIIDS'13 Proceedings of the 5th Asian conference on Intelligent Information and Database Systems - Volume Part II
A survey of multiple classifier systems as hybrid systems
Information Fusion
Cost-sensitive decision tree ensembles for effective imbalanced classification
Applied Soft Computing
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The paper presents the novel adaptive splitting and selection algorithm (AdaSS) used for learning compound pattern recognition system. Splitting a feature space into its constituents and selection of the best area classifier from the pool of available recognizers for each region are key processes of the proposed model. Both take place simultaneously as part of a compound optimization process aimed at maximizing system performance. Evolutionary algorithms are used to find out the optimal solution. The results of experiments for algorithm evaluation purposes prove the quality of the proposed approach.