# A comparative study of general fuzzy min-max neural networks for pattern classification problems

## TL;DR

This paper provides a comprehensive empirical analysis of the general fuzzy min-max neural network, examining factors affecting its performance and comparing it with other machine learning methods on benchmark datasets.

## Contribution

It offers a detailed evaluation of the GFMM neural network's performance factors, advantages, and limitations, and compares it with other classifiers.

## Key findings

- Hyperbox size significantly affects accuracy.
- Similarity measures influence agglomerative learning outcomes.
- GFMM shows competitive performance with other machine learning algorithms.

## Abstract

General fuzzy min-max (GFMM) neural network is a generalization of fuzzy neural networks formed by hyperbox fuzzy sets for classification and clustering problems. Two principle algorithms are deployed to train this type of neural network, i.e., incremental learning and agglomerative learning. This paper presents a comprehensive empirical study of performance influencing factors, advantages, and drawbacks of the general fuzzy min-max neural network on pattern classification problems. The subjects of this study include (1) the impact of maximum hyperbox size, (2) the influence of the similarity threshold and measures on the agglomerative learning algorithm, (3) the effect of data presentation order, (4) comparative performance evaluation of the GFMM with other types of fuzzy min-max neural networks and prevalent machine learning algorithms. The experimental results on benchmark datasets widely used in machine learning showed overall strong and weak points of the GFMM classifier. These outcomes also informed potential research directions for this class of machine learning algorithms in the future.