Scalars, A Way to Improve the Multi-Objective Prediction of the GAdC-Method
SBRN '00 Proceedings of the VI Brazilian Symposium on Neural Networks (SBRN'00)
Feature Selection Using Multi-Objective Genetic Algorithms for Handwritten Digit Recognition
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Feature Selection for Ensembles: A Hierarchical Multi-Objective Genetic Algorithm Approach
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Hybrid Genetic Algorithms for Feature Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Feature Selection for Ensemble of Classifiers
IWFHR '04 Proceedings of the Ninth International Workshop on Frontiers in Handwriting Recognition
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
Preferences and their application in evolutionary multiobjectiveoptimization
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Performance assessment of multiobjective optimizers: an analysis and review
IEEE Transactions on Evolutionary Computation
Multi-objective evolutionary algorithms for feature selection: application in bankruptcy prediction
SEAL'10 Proceedings of the 8th international conference on Simulated evolution and learning
An Iterative Method for Deciding SVM and Single Layer Neural Network Structures
Neural Processing Letters
MCPR'11 Proceedings of the Third Mexican conference on Pattern recognition
Application of NSGA-II to feature selection for facial expression recognition
Computers and Electrical Engineering
PRICAI'12 Proceedings of the 12th Pacific Rim international conference on Trends in Artificial Intelligence
Bi-objective feature selection for discriminant analysis in two-class classification
Knowledge-Based Systems
Computer Methods and Programs in Biomedicine
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This paper deals with the multi-objective definition of the feature selection problem for different pattern recognition domains. We use NSGA II the latest multi-objective algorithm developed for resolving problems of multi-objective aspects with more accuracy and a high convergence speed. We define the feature selection as a problem including two competing objectives and we try to find a set of optimal solutions so called Pareto-optimal solutions instead of a single optimal solution. The two competing objectives are the minimization of both the number of used features and the classification error using 1-NN classifier. We apply our method to five databases selected from the UCI repository and we report the results on these databases. We present the convergence of the NSGA II on different problems and discuss the behavior of NSGA II on these different contexts.