# An Effective Multi-Resolution Hierarchical Granular Representation based Classifier using General Fuzzy Min-Max Neural Network

## TL;DR

This paper introduces a multi-resolution hierarchical granular classifier using hyperbox fuzzy sets, which simplifies data, reduces size, and maintains high accuracy across different abstraction levels, effectively handling uncertainty.

## Contribution

It presents a novel hierarchical approach to construct classifiers from multi-resolution granular representations, improving efficiency and robustness over existing fuzzy min-max neural networks.

## Key findings

- High accuracy at low granularity levels
- Significant data size reduction
- Faster training and better performance than existing methods

## Abstract

Motivated by the practical demands for simplification of data towards being consistent with human thinking and problem solving as well as tolerance of uncertainty, information granules are becoming important entities in data processing at different levels of data abstraction. This paper proposes a method to construct classifiers from multi-resolution hierarchical granular representations (MRHGRC) using hyperbox fuzzy sets. The proposed approach forms a series of granular inferences hierarchically through many levels of abstraction. An attractive characteristic of our classifier is that it can maintain relatively high accuracy at a low degree of granularity based on reusing the knowledge learned from lower levels of abstraction. In addition, our approach can reduce the data size significantly as well as handling the uncertainty and incompleteness associated with data in real-world applications. The construction process of the classifier consists of two phases. The first phase is to formulate the model at the greatest level of granularity, while the later stage aims to reduce the complexity of the constructed model and deduce it from data at higher abstraction levels. Experimental outcomes conducted comprehensively on both synthetic and real datasets indicated the efficiency of our method in terms of training time and predictive performance in comparison to other types of fuzzy min-max neural networks and common machine learning algorithms.