One of the crucial problems of designing a classifier ensemble is the proper choice of the base classifier line-up. Basically, such an ensemble is formed on the basis of individual classifiers, which are trained in such a way to ensure their high diversity or they are chosen on the basis of pruning which reduces the number of predictive models in order to improve efficiency and predictive performance of the ensemble. This work is focusing on clustering-based ensemble pruning, which looks for the group of similar classifiers which are replaced by their representatives. We propose a novel pruning criterion based on well-known diversity measures and describe three algorithms using classifier clustering. The first method selects the model with the best predictive performance from each cluster to form the final ensemble, the second one employs the multistage organization, where instead of removing the classifiers from the ensemble each classifier cluster makes the decision independently, while the third proposition combines multistage organization and sampling with replacement. The proposed approaches were evaluated using 30 datasets with different characteristics. Experimentation results validated through statistical tests confirmed the usefulness of the proposed approaches.

中文翻译：

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新颖的基于聚类的修剪算法

设计分类器集合的关键问题之一是基本分类器阵容的正确选择。基本上，这样的集合是在单个分类器的基础上形成的，这些分类器以确保其高度多样性的方式进行训练，或者在修剪的基础上进行选择，从而减少了预测模型的数量，从而提高了效率和预测性能合奏。这项工作的重点是基于聚类的整体修剪，该修剪寻找一组由其代表代替的相似分类器。我们提出了一种基于众所周知的多样性度量的新颖修剪准则，并使用分类器聚类描述了三种算法。第一种方法是从每个聚类中选择具有最佳预测性能的模型，以形成最终整体，第二个方案采用多阶段组织，其中每个分类器集群不是从整体中删除分类器，而是独立做出决策，而第三个命题则将多阶段组织和抽样与替换结合在一起。使用具有不同特征的30个数据集对提出的方法进行了评估。通过统计测试验证的实验结果证实了所提出方法的有效性。