Multiple comparison procedures
Multiple comparison procedures
Robust classification systems for imprecise environments
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Statistical Pattern Recognition: A Review
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Ensemble learning via negative correlation
Neural Networks
Complexity Measures of Supervised Classification Problems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Neural Network Training Using Genetic Algorithms
Neural Network Training Using Genetic Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Journal of Global Optimization
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Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
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Cost-sensitive boosting for classification of imbalanced data
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IEEE Transactions on Pattern Analysis and Machine Intelligence
SMOTE: synthetic minority over-sampling technique
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IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
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A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Making use of population information in evolutionary artificialneural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Hybridization of evolutionary algorithms and local search by means of a clustering method
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Coevolving Memetic Algorithms: A Review and Progress Report
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Artificial Intelligence in Medicine
A new evolutionary system for evolving artificial neural networks
IEEE Transactions on Neural Networks
Constructive neural-network learning algorithms for pattern classification
IEEE Transactions on Neural Networks
Extraction of rules from artificial neural networks for nonlinear regression
IEEE Transactions on Neural Networks
Learning in the multiple class random neural network
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Pareto evolutionary neural networks
IEEE Transactions on Neural Networks
Mutation-based genetic neural network
IEEE Transactions on Neural Networks
An Optimization Methodology for Neural Network Weights and Architectures
IEEE Transactions on Neural Networks
RAMOBoost: ranked minority oversampling in boosting
IEEE Transactions on Neural Networks
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Pattern Recognition
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IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part II
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IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part II
Weighting Efficient Accuracy and Minimum Sensitivity for Evolving Multi-Class Classifiers
Neural Processing Letters
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Knowledge-Based Systems
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HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part II
A two-stage evolutionary algorithm based on sensitivity and accuracy for multi-class problems
Information Sciences: an International Journal
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Expert Systems with Applications: An International Journal
Hybrid multi-objective machine learning classification in liver transplantation
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part I
Information Sciences: an International Journal
Artificial Intelligence in Medicine
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Neurocomputing
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This paper proposes a multiclassification algorithm using multilayer perceptron neural network models. It tries to boost two conflicting main objectives of multiclassifiers: a high correct classification rate level and a high classification rate for each class. This last objective is not usually optimized in classification, but is considered here given the need to obtain high precision in each class in real problems. To solve this machine learning problem, we use a Pareto-based multiobjective optimization methodology based on a memetic evolutionary algorithm. We consider a memetic Pareto evolutionary approach based on the NSGA2 evolutionary algorithm (MPENSGA2). Once the Pareto front is built, two strategies or automatic individual selection are used: the best model in accuracy and the best model in sensitivity (extremes in the Pareto front). These methodologies are applied to solve 17 classification benchmark problems obtained from the University of California at Irvine (UCI) repository and one complex real classification problem. The models obtained show high accuracy and a high classification rate for each class.