Learning Gradual Argumentation Frameworks using Genetic Algorithms

TL;DR

This paper introduces a genetic algorithm approach to learn the structure of gradual argumentation frameworks for interpretable machine learning, demonstrating comparable performance to decision trees on benchmark datasets.

Contribution

It presents the first genetic algorithm method for simultaneously learning the structure and weights of argumentative classification models.

Findings

Models achieve accuracy comparable to decision trees.

The approach balances interpretability and performance.

First experimental validation on UCI datasets.

Abstract

Gradual argumentation frameworks represent arguments and their relationships in a weighted graph. Their graphical structure and intuitive semantics makes them a potentially interesting tool for interpretable machine learning. It has been noted recently that their mechanics are closely related to neural networks, which allows learning their weights from data by standard deep learning frameworks. As a first proof of concept, we propose a genetic algorithm to simultaneously learn the structure of argumentative classification models. To obtain a well interpretable model, the fitness function balances sparseness and accuracy of the classifier. We discuss our algorithm and present first experimental results on standard benchmarks from the UCI machine learning repository. Our prototype learns argumentative classification models that are comparable to decision trees in terms of learning performance and interpretability.