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
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