Original Contribution: Stacked generalization
Neural Networks
Decision Combination in Multiple Classifier Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Democracy in neural nets: voting schemes for classification
Neural Networks
A Method of Combining Multiple Experts for the Recognition of Unconstrained Handwritten Numerals
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Combination of Multiple Classifiers Using Local Accuracy Estimates
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition Letters
Combining Artificial Neural Nets: Ensemble and Modular Multi-Net Systems
Combining Artificial Neural Nets: Ensemble and Modular Multi-Net Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Selection of Classifiers Based on Multiple Classifier Behaviour
Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
Adaptive Selection of Image Classifiers
ICIAP '97 Proceedings of the 9th International Conference on Image Analysis and Processing-Volume I - Volume I
The ``Test and Select'' Approach to Ensemble Combination
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Multiple Classifier Combination Methodologies for Different Output Levels
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Adaptive mixtures of local experts
Neural Computation
Engineering multiversion neural-net systems
Neural Computation
Application of majority voting to pattern recognition: an analysis of its behavior and performance
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Ensembles of Learning Machines
WIRN VIETRI 2002 Proceedings of the 13th Italian Workshop on Neural Nets-Revised Papers
MCS '02 Proceedings of the Third International Workshop on Multiple Classifier Systems
Cascade Classifier: Design and Application to Digit Recognition
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Ensemble Learning: A Study on Different Variants of the Dynamic Selection Approach
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
Ensemble of classifiers based on hard instances
MCPR'11 Proceedings of the Third Mexican conference on Pattern recognition
Multiple classifier system for urban area's extraction from high resolution remote sensing imagery
ICIAR'11 Proceedings of the 8th international conference on Image analysis and recognition - Volume Part II
Score selection techniques for fingerprint multi-modal biometric authentication
ICIAP'05 Proceedings of the 13th international conference on Image Analysis and Processing
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
Being SMART about failures: assessing repairs in SMART homes
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
The use of artificial-intelligence-based ensembles for intrusion detection: a review
Applied Computational Intelligence and Soft Computing
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At present, the usual operation mechanism of multiple classifier systems is the combination of classifier outputs. Recently, some researchers have pointed out the potentialities of "dynamic classifier selection" as an alternative operation mechanism. However, such potentialities have been motivated so far by experimental results and qualitative arguments. This paper is aimed to provide a theoretical framework for dynamic classifier selection and to define the assumptions under which it can be expected to improve the accuracy of the individual classifiers. To this end, dynamic classifier selection is placed in the general framework of statistical decision theory and it is shown that, under some assumptions, the optimal Bayes classifier can be obtained by selecting non-optimal classifiers. Two classifier selection methods that derive from the proposed framework are described. The experimental results obtained in the classification of remote-sensing images and comparisons among different combination methods are reported.