A novel structured argumentation framework for improved explainability of classification tasks

TL;DR

This paper introduces the extend argumentative decision graph ($xADG$), a new structured argumentation framework that enhances explainability and predictive performance in classification tasks by incorporating logical operators and multiple premises.

Contribution

The paper proposes the $xADG$ framework, extending existing argumentation graphs with logical operators and multiple premises, leading to more concise and understandable models for classification.

Findings

$xADGs$ achieve strong balanced accuracy using decision tree inputs.

$xADGs$ reduce the number of supports needed for conclusions.

$xADGs$ outperform other $ADG$ techniques in size and predictive capacity.

Abstract

This paper presents a novel framework for structured argumentation, named extend argumentative decision graph ($xADG$). It is an extension of argumentative decision graphs built upon Dung's abstract argumentation graphs. The $xADG$ framework allows for arguments to use boolean logic operators and multiple premises (supports) within their internal structure, resulting in more concise argumentation graphs that may be easier for users to understand. The study presents a methodology for construction of $xADGs$ and evaluates their size and predictive capacity for classification tasks of varying magnitudes. Resulting $xADGs$ achieved strong (balanced) accuracy, which was accomplished through an input decision tree, while also reducing the average number of supports needed to reach a conclusion. The results further indicated that it is possible to construct plausibly understandable $xADGs$ that outperform other techniques for building $ADGs$ in terms of predictive capacity and overall size. In summary, the study suggests that $xADG$ represents a promising framework to developing more concise argumentative models that can be used for classification tasks and knowledge discovery, acquisition, and refinement.