Selection of Classifiers Based on Multiple Classifier Behaviour
Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
Post-processing of Classifier Outputs in Multiple Classifier Systems
MCS '02 Proceedings of the Third International Workshop on Multiple Classifier Systems
A New Evaluation Method for Expert Combination in Multi-expert System Designing
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Undesirable effects of output normalization in multiple classifier systems
Pattern Recognition Letters
Multi-Classifier Systems: Review and a roadmap for developers
International Journal of Hybrid Intelligent Systems
Non-strict heterogeneous Stacking
Pattern Recognition Letters
Pruning extensions to stacking
Intelligent Data Analysis
Dynamic integration of classifiers for handling concept drift
Information Fusion
From dynamic classifier selection to dynamic ensemble selection
Pattern Recognition
From dynamic classifier selection to dynamic ensemble selection
Pattern Recognition
Feature Extraction for Dynamic Integration of Classifiers
Fundamenta Informaticae
Genetic algorithms in classifier fusion
Applied Soft Computing
A novel dynamic fusion method using localized generalization error model
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
A new dynamic ensemble selection method for numeral recognition
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
An experimental study of one- and two-level classifier fusion for different sample sizes
Pattern Recognition Letters
Face recognition via AAM and multi-features fusion on riemannian manifolds
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part III
Acute leukemia classification by ensemble particle swarm model selection
Artificial Intelligence in Medicine
Dynamic fusion method using Localized Generalization Error Model
Information Sciences: an International Journal
Feature Extraction for Dynamic Integration of Classifiers
Fundamenta Informaticae
Improving a dynamic ensemble selection method based on oracle information
International Journal of Innovative Computing and Applications
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In the field of pattern recognition, the concept of Multiple Classifier Systems (MCSs) was proposed as a method for the development of high performance classification systems. At present, the common operation mechanism of MCSs is the combination of classifiers outputs. Recently, some researchers pointed out the potentialities of dynamic classifier selection as a new operation mechanism. In a previous paper, the authors discussed the advantages of selection-based MCSs and proposed an algorithm for dynamic classifier selection. In this paper, a theoretical framework for dynamic classifier selection is described and two methods for selecting classifiers are proposed. Reported results on the classification of different data sets show that dynamic classifier selection is an effective method for the development of MCSs.