Castro Pablo A Dalbem, Von Zuben Fernando José
Department of Electrical and Computer Engineering, University of Campinas, Campinas, Säo Paulo 13083-970, Brazil.
IEEE Trans Neural Netw. 2011 Feb;22(2):304-16. doi: 10.1109/TNN.2010.2096823. Epub 2010 Dec 23.
In this paper, we apply an immune-inspired approach to design ensembles of heterogeneous neural networks for classification problems. Our proposal, called Bayesian artificial immune system, is an estimation of distribution algorithm that replaces the traditional mutation and cloning operators with a probabilistic model, more specifically a Bayesian network, representing the joint distribution of promising solutions. Among the additional attributes provided by the Bayesian framework inserted into an immune-inspired search algorithm are the automatic control of the population size along the search and the inherent ability to promote and preserve diversity among the candidate solutions. Both are attributes generally absent from alternative estimation of distribution algorithms, and both were shown to be useful attributes when implementing the generation and selection of components of the ensemble, thus leading to high-performance classifiers. Several aspects of the design are illustrated in practical applications, including a comparative analysis with other attempts to synthesize ensembles.
在本文中，我们应用一种受免疫启发的方法来设计用于分类问题的异构神经网络集成。我们提出的方法称为贝叶斯人工免疫系统，它是一种分布估计算法，用概率模型（更具体地说是贝叶斯网络）取代传统的变异和克隆算子，该概率模型表示有希望的解决方案的联合分布。插入受免疫启发的搜索算法中的贝叶斯框架所提供的其他属性包括在搜索过程中对种群大小的自动控制以及促进和保持候选解决方案之间多样性的固有能力。这两个属性通常在替代的分布估计算法中不存在，并且在实现集成组件的生成和选择时都被证明是有用的属性，从而产生高性能的分类器。设计的几个方面在实际应用中得到了说明，包括与其他合成集成的尝试的比较分析。
