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.
翻译：在本条中,引入了一个新的Fuzzy Grantural Approcistication 分类(FGAC) 。 分类法基于先前采用的颗粒近似及其多级分类案例的概念。 分类法以实例为基础, 其最大的优势在于其地方透明度, 即能够解释其作出的每一项预测。 我们首先为二进制分类案例和多级分类案例开发FGAC, 并讨论其变异性, 包括有条理的加权平均操作员。 然后, FGAC的这些变异性与其他本地透明的 ML 方法相比是经验化的。 最后, 我们讨论FGAC的透明度及其相对于其他本地透明方法的优势。 我们的结论是,尽管FGAC与其他本地透明的 ML 模型具有相似的预测性能, 但在某些情况下,其透明度可能优于其他本地透明的 ML 模式 。
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