Multiobjective optimization of ensembles of multilayer perceptrons for pattern classification

Authors:
P. A. Castillo;M. G. Arenas;J. J. Merelo;V. M. Rivas;G. Romero
Affiliations:
Department of Architecture and Computer Technology, University of Granada, (Spain);Department of Computer Science, University of Jaén, (Spain);Department of Architecture and Computer Technology, University of Granada, (Spain);Department of Computer Science, University of Jaén, (Spain);Department of Architecture and Computer Technology, University of Granada, (Spain)
Venue:
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Year:
2006

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Abstract

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.