Ensembles of classifiers deserve attention because their stability and accuracy are usually superior compared to the single classifier. One of the aspects regarding the construction of multiple classifier systems is the fusion of each base model output. The state-of-the-art fusion of base classifiers approaches uses class labels, a rank array, or a score function to determine the classifier ensemble’s final decision. On the other hand, in this study, we use the base classifiers’ decision boundaries in the fusion process. Therefore the integration process occurs in a geometric space. In this paper, a new definition of the function that measures the central tendency has been proposed. This function allows integrating any number of linear base classifiers in the geometry space, removing the limit on the number of these classifiers in the ensemble. The limit on the number of base classifiers is noticeable in our earlier works. The proposal was compared with other fusion approaches to base classifiers outputs. The experiments on multiple binary datasets from UCI and KEEL datasets repositories demonstrate the effectiveness of our proposal of the fusion process in the geometric space. To discuss the results of our experiments, we use standard and imbalanced datasets separately.

分类器的集成值得关注，因为它们的稳定性和准确性通常优于单个分类器。关于构建多个分类器系统的一个方面是每个基础模型输出的融合。最先进的基分类器融合方法使用类标签、秩数组或评分函数来确定分类器集成的最终决策。另一方面，在本研究中，我们在融合过程中使用基分类器的决策边界。因此积分过程发生在几何空间中。在本文中，提出了衡量集中趋势的函数的新定义。此函数允许在几何空间中集成任意数量的线性基分类器，从而消除对集成中这些分类器数量的限制。在我们早期的工作中，基本分类器的数量限制是显而易见的。该提议与其他融合方法进行了基分类器输出的比较。来自 UCI 和 KEEL 数据集存储库的多个二进制数据集的实验证明了我们提出的几何空间融合过程的有效性。为了讨论我们的实验结果，我们分别使用标准和不平衡数据集。