A Method of Combining Multiple Experts for the Recognition of Unconstrained Handwritten Numerals
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combination of Multiple Classifiers Using Local Accuracy Estimates
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM Computing Surveys (CSUR)
Clustering Algorithms
Methods for Dynamic Classifier Selection
ICIAP '99 Proceedings of the 10th International Conference on Image Analysis and Processing
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Multistrategy Ensemble Learning: Reducing Error by Combining Ensemble Learning Techniques
IEEE Transactions on Knowledge and Data Engineering
A Dynamic Classifier Selection Method to Build Ensembles using Accuracy and Diversity
SBRN '06 Proceedings of the Ninth Brazilian Symposium on Neural Networks
Multi-Classifier Systems: Review and a roadmap for developers
International Journal of Hybrid Intelligent Systems
Classifier Ensembles with a Random Linear Oracle
IEEE Transactions on Knowledge and Data Engineering
From dynamic classifier selection to dynamic ensemble selection
Pattern Recognition
An empirical evaluation of constructive neural network algorithms in classification tasks
International Journal of Innovative Computing and Applications
Static and dynamic selection of ensemble of classifiers
Static and dynamic selection of ensemble of classifiers
Dynamic Classifier Ensemble Selection Based on GMDH
CSO '09 Proceedings of the 2009 International Joint Conference on Computational Sciences and Optimization - Volume 01
Cluster-based classification using self-organising maps for medical image databases
International Journal of Innovative Computing and Applications
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
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This work evaluates some strategies to approximate the performance of a dynamic ensemble selection method to the oracle performance of its pool of weak classifiers. For this purpose, we evaluated different distance metrics in the K-nearest-oracles (KNORA) method, the use of statistics related to the class accuracy of each classifier in the pool and some additional information calculated by using a clustering process in the validation dataset. Moreover, different strategies are also evaluated to combine the results of the KNORA dynamic ensemble selection method with the results of its built-in K-nearest neighbour (KNN) used to define the neighbourhood of a test pattern during the ensemble creation. A strong experimental protocol based on more than 60,000 samples of handwriting digits extracted from NIST-SD19 was used to evaluate each strategy. The experiments have shown that the fusion of the KNORA results with the results of its built-in KNN is a very promising strategy.