Fuzzy granular approximation classifier
This work addresses the need for interpretable AI by developing a locally transparent classifier, though it is incremental as it builds on existing granular approximation concepts.
The authors tackled the problem of local transparency in machine learning classifiers by introducing the Fuzzy Granular Approximation Classifier (FGAC), which provides explanations for individual predictions, and found that it achieves similar predictive performance to other locally transparent models while offering superior transparency in some cases.
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.