# Fuzzy granular approximation classifier

arXiv: https://arxiv.org/abs/2206.01240 · HTML: https://mlsanity.com/paper/2206.01240/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 16 references that are in the map.
- First posted on arXiv 2 Jun 2022.

## Builds on (most cited first, 3 of 3)

- [Should we really use post-hoc tests based on mean-ranks?](https://mlsanity.com/paper/1505.02288/) (2015)
- [A Novel Machine Learning Approach to Data Inconsistency with respect to a Fuzzy Relation](https://mlsanity.com/paper/2111.13447/) (2021)
- [Multi-class granular approximation by means of disjoint and adjacent fuzzy granules](https://mlsanity.com/paper/2202.07584/) (2022)

## Abstract (arXiv)

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.