This paper has three main goals: i) to employ an immune-based algorithm to train multi-layer perceptron (MLP) neural networks for pattern classification; ii) to combine the trained neural networks into ensembles of classifiers; and iii) to investigate the influence of diversity in the classification performance of individual and ensembles of classifiers. Two different classes of algorithms to train MLP are tested: bio-inspired, and gradient-based. Comparisons among all the training methods are presented in terms of classification accuracy and diversity of the solutions found. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Pasti, R., & De Castro, L. N. (2007). The influence of diversity in an immune-based algorithm to train MLP networks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4628 LNCS, pp. 71–82). Springer Verlag. https://doi.org/10.1007/978-3-540-73922-7_7
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