论文标题
模糊粒状近似分类器
Fuzzy granular approximation classifier
论文作者
论文摘要
在本文中，引入了新的模糊颗粒近似分类器（FGAC）。分类器基于先前介绍的颗粒近似概念及其多类分类案例。该分类器基于实例，其最大优势是其本地透明度，即解释其做出的每个单独预测的能力。我们首先为二进制分类案例和多类分类案例开发FGAC，并讨论其变化，其中包括有序的加权平均（OWA）操作员。然后，FGAC的那些变化与其他局部透明的ML方法进行了经验比较。最后，我们讨论了FGAC的透明度及其优于其他局部透明方法的优势。我们得出的结论是，尽管FGAC具有与其他局部透明ML模型相似的预测性能，但在某些情况下，其透明度可以更高。
In this article, a new Fuzzy Granular Approximation Classifier (FGAC) is introduced. The classifier is based on the previously introduced concept of the granular approximation and its multi-class classification case. The classifier is instance-based and its biggest advantage is its local transparency i.e., the ability to explain every individual prediction it makes. We first develop the FGAC for the binary classification case and the multi-class classification case and we discuss its variation that includes the Ordered Weighted Average (OWA) operators. Those variations of the FGAC are then empirically compared with other locally transparent ML methods. At the end, we discuss the transparency of the FGAC and its advantage over other locally transparent methods. We conclude that while the FGAC has similar predictive performance to other locally transparent ML models, its transparency can be superior in certain cases.
