Although linear classifiers are one of the oldest methods in machine learning, they are still very popular in the machine learning community. This is due to their low computational complexity and robustness to overfitting. Consequently, linear classifiers are often used as base classifiers of multiple ensemble classification systems. This research is aimed at building a new fusion method dedicated to the ensemble of linear classifiers. The fusion scheme uses both measurement space and geometrical space. Namely, we proposed a probability-driven scoring function which shape depends on the orientation of the decision hyperplanes generated by the base classifiers. The proposed fusion method is compared with the reference method using multiple benchmark datasets taken from the KEEL repository. The comparison is done using multiple quality criteria. The statistical analysis of the obtained results is also performed. The experimental study shows that, under certain conditions, some improvement may be obtained.

翻译：虽然线性分类是机器学习中最古老的方法之一,但在机器学习中仍然非常受欢迎。 这是因为其计算复杂性低,而且过于适合, 因此, 线性分类经常用作多种混合分类系统的基础分类器。 这项研究旨在建立一个专门用于线性分类器组合的新的聚合方法。 聚合方案使用测量空间和几何空间。 也就是说, 我们提议一种概率驱动的评分功能, 其形状取决于基级分类器生成的决定超高平面的取向。 拟议的聚变方法与参考方法相比较, 使用从 KEEL 仓库获取的多个基准数据集。 比较工作采用多重质量标准。 对所获得的结果进行统计分析。 实验研究表明, 在某些条件下, 可能会取得一些改进。