The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Classification for Imprecise Environments
Machine Learning
Learning Decision Trees Using the Area Under the ROC Curve
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Multi-class ROC analysis from a multi-objective optimisation perspective
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
An ensemble classifier learning approach to ROC optimization
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
Threshold-optimized decision-level fusion and its application to biometrics
Pattern Recognition
A new framework for adaptive multimodal biometrics management
IEEE Transactions on Information Forensics and Security
AUC analysis of the pareto-front using multi-objective GP for classification with unbalanced data
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Evolving ensembles in multi-objective genetic programming for classification with unbalanced data
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Adaptive ROC-based ensembles of HMMs applied to anomaly detection
Pattern Recognition
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Fusion of biometric systems using Boolean combination: an application to iris-based authentication
International Journal of Biometrics
On Linear Combinations of Dichotomizers for Maximizing the Area Under the ROC Curve
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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In this paper, we propose a novel approach for the multi-objective optimization of classifier ensembles in the ROC space. We first evolve a pool of simple classifiers with NSGA-II using values of the ROC curves as the optimization objectives. These simple classifiers are then combined at the decision level using the Iterative Boolean Combination method (IBC). This method produces multiple ensembles of classifiers optimized for various operating conditions. We perform a rigorous series of experiments to demonstrate the properties and behaviour of this approach. This allows us to propose interesting venues for future research on optimizing ensembles of classifiers using multi-objective evolutionary algorithms.