Supported Abstract Argumentation for Case-Based Reasoning

TL;DR

This paper presents sAA-CBR, a novel case-based reasoning model that uses argumentation with supports to improve classification accuracy by eliminating extraneous cases, ensuring more reliable debates.

Contribution

The paper introduces sAA-CBR, an enhanced argumentation-based CBR model that incorporates supports to prevent extraneous cases, improving debate quality without sacrificing key properties.

Findings

sAA-CBR contains no extraneous cases

sAA-CBR maintains core model properties

The model improves classification reliability

Abstract

We introduce Supported Abstract Argumentation for Case-Based Reasoning (sAA-CBR), a binary classification model in which past cases engage in debates by arguing in favour of their labelling and attacking or supporting those with opposing or agreeing labels. With supports, sAA-CBR overcomes the limitation of its precursor AA-CBR, which can contain extraneous cases (or spikes) that are not included in the debates. We prove that sAA-CBR contains no spikes, without trading off key model properties