Journal of Global Optimization
Differential Evolution Training Algorithm for Feed-Forward Neural Networks
Neural Processing Letters
Speeding up backpropagation using multiobjective evolutionary algorithms
Neural Computation
Rapid and brief communication: Evolutionary extreme learning machine
Pattern Recognition
Sensitivity versus accuracy in multiclass problems using memetic Pareto evolutionary neural networks
IEEE Transactions on Neural Networks
Universal approximation using incremental constructive feedforward networks with random hidden nodes
IEEE Transactions on Neural Networks
On the performance of the µ-GA extreme learning machines in regression problems
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part II
A hybrid evolutionary approach to obtain better quality classifiers
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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Accuracy alone is insufficient to evaluate the performance of a classifier especially when the number of classes increases This paper proposes an approach to deal with multi-class problems based on Accuracy (C) and Sensitivity (S) We use the differential evolution algorithm and the ELM-algorithm (Extreme Learning Machine) to obtain multi-classifiers with a high classification rate level in the global dataset with an acceptable level of accuracy for each class This methodology is applied to solve four benchmark classification problems and obtains promising results.