| Title |
Memetic pareto differential evolution for designing artificial neural networks in multiclassification problems using cross-entropy versus sensitivity |
| Authors |
Fernández J.C. , Hervás C. , MARTÍNEZ ESTUDILLO, FRANCISCO JOSÉ, Gutiérrez P.A. , Cruz M. |
| External publication |
No |
| Means |
Lecture Notes in Computer Science |
| Scope |
Conference Paper |
| Nature |
Científica |
| JCR Quartile |
4 |
| SJR Quartile |
2 |
| SJR Impact |
0.302 |
| Web |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-70350633267&doi=10.1007%2f978-3-642-02319-4_52&partnerID=40&md5=43d72175e92a0b5c54f2cd3d09f1595b |
| Publication date |
01/01/2009 |
| Scopus Id |
2-s2.0-70350633267 |
| DOI |
10.1007/978-3-642-02319-4_52 |
| Abstract |
This work proposes a Multiobjective Differential Evolution algorithm based on dominance Pareto concept for multiclassification problems using multilayer perceptron neural network models. The algorithm include a local search procedure and optimizes two conflicting objectives of multiclassifiers, a high correct classification rate and a high classification rate for each class, of which the latter is not usually optimized in classification. Once the Pareto front is built, we use two automatic selection methodologies of individuals: the best model with respect to accuracy and the best model with respect to sensitivity (extremes in the Pareto front). These strategies are applied to solve six classification benchmark problems obtained from the UCI repository. The models obtained show a high accuracy and a high classification rate for each class. © 2009 Springer Berlin Heidelberg. |
| Keywords |
Accuracy; Differential Evolution; Local Search; Multiclassification; Multiobjective; Pareto; Sensitivity; Multiobjective optimization; Neural networks; Pareto principle; Evolutionary algorithms |
| Universidad Loyola members |
|
Manage cookies