Making large-scale support vector machine learning practical
Advances in kernel methods
The bias-variance tradeoff and the randomized GACV
Proceedings of the 1998 conference on Advances in neural information processing systems II
Robust Classification for Imprecise Environments
Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Choosing Multiple Parameters for Support Vector Machines
Machine Learning
Learning Decision Trees Using the Area Under the ROC Curve
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization
Proceedings of the 5th International Conference on Genetic Algorithms
Support Vector Machines: Training and Applications
Support Vector Machines: Training and Applications
Ensemble selection from libraries of models
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Gradient-Based Optimization of Hyperparameters
Neural Computation
A two-stage outlier rejection strategy for numerical field extraction in handwritten documents
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Multiclass SVM Model Selection Using Particle Swarm Optimization
HIS '06 Proceedings of the Sixth International Conference on Hybrid Intelligent Systems
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
Multi-objective learning via genetic algorithms
IJCAI'85 Proceedings of the 9th international joint conference on Artificial intelligence - Volume 1
Automatic model selection for the optimization of SVM kernels
Pattern Recognition
An EA multi-model selection for SVM multiclass schemes
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
DAS'06 Proceedings of the 7th international conference on Document Analysis Systems
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
An online AUC formulation for binary classification
Pattern Recognition
Multi-objective evolutionary optimization for generating ensembles of classifiers in the ROC space
Proceedings of the 14th annual conference on Genetic and evolutionary computation
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In this paper, we tackle the problem of model selection when misclassification costs are unknown and/or may evolve. Unlike traditional approaches based on a scalar optimization, we propose a generic multi-model selection framework based on a multi-objective approach. The idea is to automatically train a pool of classifiers instead of one single classifier, each classifier in the pool optimizing a particular trade-off between the objectives. Within the context of two-class classification problems, we introduce the ''ROC front concept'' as an alternative to the ROC curve representation. This strategy is applied to the multi-model selection of SVM classifiers using an evolutionary multi-objective optimization algorithm. The comparison with a traditional scalar optimization technique based on an AUC criterion shows promising results on UCI datasets as well as on a real-world classification problem.