Hybrid Pareto differential evolutionary artificial neural networks to determined growth multi-classes in predictive microbiology

Authors:
M. Cruz-Ramírez;J. Sánchez-Monedero;F. Fernández-Navarro;J. C. Fernández;C. Hervás-Martínez
Affiliations:
Department of Computer Science and Numerical Analysis, University of Córdoba, Córdoba, Spain;Department of Computer Science and Numerical Analysis, University of Córdoba, Córdoba, Spain;Department of Computer Science and Numerical Analysis, University of Córdoba, Córdoba, Spain;Department of Computer Science and Numerical Analysis, University of Córdoba, Córdoba, Spain;Department of Computer Science and Numerical Analysis, University of Córdoba, Córdoba, Spain
Venue:
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part III
Year:
2010

Citing 3
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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
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)

Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Memetic Pareto Differential Evolution for Designing Artificial Neural Networks in Multiclassification Problems Using Cross-Entropy Versus Sensitivity

HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems

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Abstract

The main objective of this work is to automatically design artificial neural network, ANN, models with sigmoid basis units for multiclassification tasks in predictive microbiology. The classifiers obtained achieve a double objective: high classification level in the dataset and high classification level for each class. For learning, the structure and weights of the ANN we present an Hybrid Pareto Differential Evolution Neural Network (HPDENN), a Differential Evolutionary approach based on the PDE multiobjective evolutionary algorithm. The PDE algorithm is augmented with a local search using the improved Resilient Backpropagation with backtraking-IRprop+ algorithm. To analyze the robustness of this methodology, we have applied it to two complex problems of classification in predictive microbiology (Staphylococcus Aureus and Shigella Flexneri). The results obtained in Correct Classification Rate (C) and Minimum Sensitivity (S) for each class show that the generalization ability and the classification rate in each class can be more efficiently improved within this multiobjective algorithm.