Centrality measures have been helping to explain the behavior of objects, given their relation, in a wide variety of problems, since sociology to chemistry. This work considers these measures to assess the importance of every classifier belonging to an ensemble of classifiers, aiming to improve a Multiple Classifier System (MCS). Assessing the classifier’s importance by employing centrality measures, inspired two different approaches: one for selecting classifiers and another for fusion. The selection approach, called Centrality Based Selection (CBS), adopts a trade-off between the classifier’s accuracy and their diversity. The sub-optimal selected subset presents good results against selection methods from the literature, being superior in 67.22% of the cases. The second approach, the integration, is named Centrality Based Fusion (CBF). This approach is a weighted combination method, which is superior to literature in 70% of the cases.

中文翻译：

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基于中心性度量的分类器选择和组合

从社会学到化学，考虑到它们在各种问题中的关系，中心性测量一直有助于解释对象的行为。这项工作考虑了这些措施来评估属于分类器集合的每个分类器的重要性，旨在改进多分类器系统（MCS）。通过采用中心性度量来评估分类器的重要性，启发了两种不同的方法：一种用于选择分类器，另一种用于融合。这种选择方法称为基于中心性的选择（CBS），它在分类器的准确性和它们的多样性之间进行权衡。次优选择子集与文献中的选择方法相比表现出良好的结果，在 67.22% 的情况下优于。第二种方法，即集成，称为基于中心性的融合 (CBF)。