Fusion of linear base classifiers in geometric space

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. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

Ensembles of classifiers are known for their stability and accuracy, often outperforming single classifiers. This study proposes a fusion method in geometric space using decision boundaries of base classifiers, introducing a new function for measuring central tendency and removing the limit on the number of base classifiers. Experiments on multiple binary datasets show the effectiveness of this approach.