In this paper, we consider a system in which knowledge in a dispersed form is available. In the system local classifiers are combined into coalitions. Two methods of combining classifiers in coalitions are discussed in this paper — with a hierarchical agglomeration algorithm and with Pawlak’s conflict model. The purpose of this paper is to apply methods for reducing dimensionality in these two approaches. Two methods of attribute reduction are considered — based on the rough set theory and based on attribute correlation with decision class. The most important conclusions formulated in the paper are as follows. The use of attribute selection method improves the quality of classification of the dispersed system. Better results are generated by the system with a hierarchical agglomeration algorithm.

中文翻译：

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在分散的决策系统中生成分类器联盟的两种方法和降低维数的两种方法的比较

在本文中，我们考虑了一个系统，其中可以使用分散形式的知识。在系统中，本地分类器被组合成联盟。本文讨论了在联盟中组合分类器的两种方法——分层凝聚算法和 Pawlak 冲突模型。本文的目的是在这两种方法中应用降低维度的方法。考虑了两种属性约简方法——基于粗糙集理论和基于与决策类的属性相关性。本文提出的最重要的结论如下。属性选择方法的使用提高了分散系统的分类质量。系统采用分层凝聚算法产生更好的结果。