Pattern classification seeks to minimize error of unknown patterns, however, in many real world applications, type I (false positive) and type II (false negative) errors have to be dealt with separately, which is a complex problem since an attempt to minimize one of them usually makes the other grow. Actually, a type of error can be more important than the other, and a trade-off that minimizes the most important error type must be reached. Despite the importance of type-II errors, most pattern classification methods take into account only the global classification error. In this paper we propose to optimize both error types in classification by means of a multiobjective algorithm in which each error type and the network size is an objective of the fitness function. A modified version of the GProp method (optimization and design of multilayer perceptrons) is used, to simultaneously optimize the network size and the type I and II errors. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Castillo, P. A., Arenas, M. G., Merelo, J. J., Rivas, V. M., & Romero, G. (2006). Multiobjective optimization of ensembles of multilayer perceptrons for pattern classification. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4193 LNCS, pp. 453–462). Springer Verlag. https://doi.org/10.1007/11844297_46
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