Machine Learning
Neural networks for pattern recognition
Neural networks for pattern recognition
An updated survey of GA-based multiobjective optimization techniques
ACM Computing Surveys (CSUR)
Journal of Global Optimization
Differential Evolution Training Algorithm for Feed-Forward Neural Networks
Neural Processing Letters
A Memetic Pareto Evolutionary Approach to Artificial Neural Networks
AI '01 Proceedings of the 14th Australian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Universal Approximation and QoS Violation Application of Extreme Learning Machine
Neural Processing Letters
Rapid and brief communication: Evolutionary extreme learning machine
Pattern Recognition
OP-ELM: optimally pruned extreme learning machine
IEEE Transactions on Neural Networks
Sensitivity versus accuracy in multiclass problems using memetic Pareto evolutionary neural networks
IEEE Transactions on Neural Networks
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Evolutionary learning using a sensitivity-accuracy approach for classification
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part II
An overview of statistical learning theory
IEEE Transactions on Neural Networks
Universal approximation using incremental constructive feedforward networks with random hidden nodes
IEEE Transactions on Neural Networks
PCA-ELM: A Robust and Pruned Extreme Learning Machine Approach Based on Principal Component Analysis
Neural Processing Letters
Exploitation of pairwise class distances for ordinal classification
Neural Computation
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Recently, a multi-objective Sensitivity---Accuracy based methodology has been proposed for building classifiers for multi-class problems. This technique is especially suitable for imbalanced and multi-class datasets. Moreover, the high computational cost of multi-objective approaches is well known so more efficient alternatives must be explored. This paper presents an efficient alternative to the Pareto based solution when considering both Minimum Sensitivity and Accuracy in multi-class classifiers. Alternatives are implemented by extending the Evolutionary Extreme Learning Machine algorithm for training artificial neural networks. Experiments were performed to select the best option after considering alternative proposals and related methods. Based on the experiments, this methodology is competitive in Accuracy, Minimum Sensitivity and efficiency.