Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Combination of Multiple Classifiers Using Local Accuracy Estimates
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combining Decision Trees and Neural Networks for Drug Discovery
EuroGP '02 Proceedings of the 5th European Conference on Genetic Programming
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
An analysis of diversity measures
Machine Learning
GNeurAge: An Evolutionary Agent-Based System for Classification Tasks
HIS '06 Proceedings of the Sixth International Conference on Hybrid Intelligent Systems
Comparison of classifier selection methods for improving committee performance
MCS'03 Proceedings of the 4th international conference on Multiple classifier systems
A new ensemble diversity measure applied to thinning ensembles
MCS'03 Proceedings of the 4th international conference on Multiple classifier systems
Pareto-Based Multiobjective Machine Learning: An Overview and Case Studies
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Evolutionary ensembles with negative correlation learning
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
A multiagent genetic algorithm for global numerical optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An experimental bias-variance analysis of SVM ensembles based on resampling techniques
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A constructive algorithm for training cooperative neural network ensembles
IEEE Transactions on Neural Networks
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Multi-classifier systems, also known as ensembles or committees, have been widely used to solve several classification problems, because they usually provide better performance than the individual classifiers. However, in order to build robust ensembles, it is necessary that the individual classifiers are as accurate as diverse among themselves - this is known as the diversity/accuracy dilemma. In this sense, some works analyzing the ensemble performance in context of this dilemma have been proposed. However, the majority of them address the homogenous structures of ensemble, i.e., ensembles composed only of the same type of classifiers. Thus, motivated by this limitation, this paper will perform an empirical investigation on the diversity/accuracy dilemma for heterogeneous ensembles. In order to do this, genetic algorithms will be used to guide the building of the ensemble systems.