Machine Learning
Combination of Multiple Classifiers Using Local Accuracy Estimates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Performance and Diversity Evaluation in Hybrid and Non-Hybrid Structures of Ensembles
HIS '05 Proceedings of the Fifth International Conference on Hybrid Intelligent Systems
Switching between selection and fusion in combining classifiers: anexperiment
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Non-parametric bootstrap ensembles for detection of tumor lesions
Pattern Recognition Letters
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part I
Ensemble classification based on generalized additive models
Computational Statistics & Data Analysis
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Ensemble-based methods for cancellable biometrics
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part I
A probabilistic model of classifier competence for dynamic ensemble selection
Pattern Recognition
Ensembles of ARTMAP-based neural networks: an experimental study
Applied Intelligence
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part II
Ensemble of classifiers based on hard instances
MCPR'11 Proceedings of the Third Mexican conference on Pattern recognition
An experimental study of one- and two-level classifier fusion for different sample sizes
Pattern Recognition Letters
Using diversity in classifier set selection for arabic handwritten recognition
MCS'10 Proceedings of the 9th international conference on Multiple Classifier Systems
ReinSel: A class-based mechanism for feature selection in ensemble of classifiers
Applied Soft Computing
Bi-objective genetic algorithm for feature selection in ensemble systems
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part I
Investigating fusion approaches in multi-biometric cancellable recognition
Expert Systems with Applications: An International Journal
Filter-based optimization techniques for selection of feature subsets in ensemble systems
Expert Systems with Applications: An International Journal
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One of the most important steps in the design of a multi-classifier system (MCS), also known as ensemble, is the choice of the components (classifiers). This step is very important to the overall performance of a MCS since the combination of a set of identical classifiers will not outperform the individual members. The ideal situation would be a set of classifiers with uncorrelated errors - they would be combined in such a way as to minimize the effect of these failures. This paper presents an extensive evaluation of how the choice of the components (classifiers) can affect the performance of several combination methods (selection-based and fusion-based methods). An analysis of the diversity of the MCSs when varying their components is also performed. As a result of this analysis, it is aimed to help designers in the choice of the individual classifiers and combination methods of an ensemble.