Combination of Multiple Classifiers Using Local Accuracy Estimates
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Boosting in the limit: maximizing the margin of learned ensembles
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Automatic Recognition of Handwritten Numerical Strings: A Recognition and Verification Strategy
IEEE Transactions on Pattern Analysis and Machine Intelligence
Methods for Dynamic Classifier Selection
ICIAP '99 Proceedings of the 10th International Conference on Image Analysis and Processing
Optimizing Nearest Neighbour in Random Subspaces using a Multi-Objective Genetic Algorithm
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Switching between selection and fusion in combining classifiers: anexperiment
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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
A dynamic classifier ensemble selection approach for noise data
Information Sciences: an International Journal
A probabilistic model of classifier competence for dynamic ensemble selection
Pattern Recognition
Global/local hybrid learning of mixture-of-experts from labeled and unlabeled data
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part I
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part II
Dynamic ensemble selection for off-line signature verification
MCS'11 Proceedings of the 10th international conference on Multiple classifier systems
Dynamic classifier ensemble model for customer classification with imbalanced class distribution
Expert Systems with Applications: An International Journal
Music genre classification using LBP textural features
Signal Processing
Ensemble approaches for regression: A survey
ACM Computing Surveys (CSUR)
Improving a dynamic ensemble selection method based on oracle information
International Journal of Innovative Computing and Applications
Expert Systems with Applications: An International Journal
Robust visual tracking using dynamic classifier selection with sparse representation of label noise
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part III
An ensemble of SVM classifiers based on gene pairs
Computers in Biology and Medicine
International Journal of Knowledge-based and Intelligent Engineering Systems
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In handwritten pattern recognition, the multiple classifier system has been shown to be useful for improving recognition rates. One of the most important tasks in optimizing a multiple classifier system is to select a group of adequate classifiers, known as an Ensemble of Classifiers (EoC), from a pool of classifiers. Static selection schemes select an EoC for all test patterns, and dynamic selection schemes select different classifiers for different test patterns. Nevertheless, it has been shown that traditional dynamic selection performs no better than static selection. We propose four new dynamic selection schemes which explore the properties of the oracle concept. Our results suggest that the proposed schemes, using the majority voting rule for combining classifiers, perform better than the static selection method.