文章基本信息

标题：Parallel Multiobjective Optimization of Ensembles of Multilayer Perceptrons for pattern classification
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作者：Pedro A. Castillo ; Juan J. Melero-Guervós
期刊名称：Inteligencia Artificial : Ibero-American Journal of Artificial Intelligence
印刷版ISSN：1137-3601
电子版ISSN：1988-3064
出版年度：2008
卷号：12
期号：38
页码：5-25
出版社：Spanish Association for Intelligence Artificial
摘要：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. Due to the cost of running the evolutionary algorithm, we propose to paralelize the method using the island model to distribute the computational load in an heterogeneous network.