Fuzzy granular approximation classifier

TL;DR

The paper introduces a new Fuzzy Granular Approximation Classifier (FGAC) that offers local transparency for predictions, with variations including OWA operators, and compares its performance and transparency to other local ML methods.

Contribution

The paper presents the FGAC, a novel instance-based classifier that emphasizes local transparency and extends it with OWA operators for improved interpretability.

Findings

FGAC achieves comparable predictive accuracy to other transparent models.

FGAC provides superior local transparency in certain scenarios.

Empirical comparisons demonstrate FGAC's effectiveness and interpretability.

Abstract

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.