Neural network learning and expert systems
Neural network learning and expert systems
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
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
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
Speeding up backpropagation using multiobjective evolutionary algorithms
Neural Computation
Data mining in metric space: an empirical analysis of supervised learning performance criteria
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Machine Learning
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Evolutionary product unit based neural networks for regression
Neural Networks
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
A genetic algorithms based multi-objective neural net applied to noisy blast furnace data
Applied Soft Computing
Neural network classification of homomorphic segmented heart sounds
Applied Soft Computing
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Multiobjective optimization using variable complexity modelling for control system design
Applied Soft Computing
Empirical investigation of the benefits of partial lamarckianism
Evolutionary Computation
Evolutionary product-unit neural networks classifiers
Neurocomputing
Applied Soft Computing
Corrections to "Pareto-based multiobjective machine learning: An overview and case studies"
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Applications of multi-objective structure optimization
Neurocomputing
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Classification of adaptive memetic algorithms: a comparative study
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Coevolving Memetic Algorithms: A Review and Progress Report
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An evolutionary artificial neural networks approach for breast cancer diagnosis
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
IEEE Transactions on Neural Networks
An evolutionary algorithm that constructs recurrent neural networks
IEEE Transactions on Neural Networks
A dynamic over-sampling procedure based on sensitivity for multi-class problems
Pattern Recognition
A multi-objective neural network based method for cover crop identification from remote sensed data
Expert Systems with Applications: An International Journal
Artificial Intelligence in Medicine
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The main objective of this work is to automatically design neural network models with sigmoid basis units for binary classification tasks. The classifiers that are obtained achieve a double objective: a high classification level in the dataset and a high classification level for each class. We present MPENSGA2, a Memetic Pareto Evolutionary approach based on the NSGA2 multiobjective evolutionary algorithm which has been adapted to design Artificial Neural Network models, where the NSGA2 algorithm is augmented with a local search that uses the improved Resilient Backpropagation with backtracking-IRprop+ algorithm. To analyze the robustness of this methodology, it was applied to four complex classification problems in predictive microbiology to describe the growth/no-growth interface of food-borne microorganisms such as Listeria monocytogenes, Escherichia coli R31, Staphylococcus aureus and Shigella flexneri. The results obtained in Correct Classification Rate (CCR), Sensitivity (S) as the minimum of sensitivities for each class, Area Under the receiver operating characteristic Curve (AUC), and Root Mean Squared Error (RMSE), show that the generalization ability and the classification rate in each class can be more efficiently improved within a multiobjective framework than within a single-objective framework.