Preference-Based Abstract Argumentation for Case-Based Reasoning (with Appendix)

TL;DR

This paper introduces AA-CBR-P, a novel method combining user preferences with abstract argumentation and case-based reasoning to improve interpretable classification, demonstrated on a medical dataset.

Contribution

It presents a new preference-based abstract argumentation framework for case-based reasoning, allowing explicit preference modeling over comparison approaches.

Findings

Outperforms existing interpretable models on a clinical dataset

Effectively incorporates user preferences into case comparison

Proves model adherence to user-defined preferences

Abstract

In the pursuit of enhancing the efficacy and flexibility of interpretable, data-driven classification models, this work introduces a novel incorporation of user-defined preferences with Abstract Argumentation and Case-Based Reasoning (CBR). Specifically, we introduce Preference-Based Abstract Argumentation for Case-Based Reasoning (which we call AA-CBR-P), allowing users to define multiple approaches to compare cases with an ordering that specifies their preference over these comparison approaches. We prove that the model inherently follows these preferences when making predictions and show that previous abstract argumentation for case-based reasoning approaches are insufficient at expressing preferences over constituents of an argument. We then demonstrate how this can be applied to a real-world medical dataset sourced from a clinical trial evaluating differing assessment methods of patients with a primary brain tumour. We show empirically that our approach outperforms other interpretable machine learning models on this dataset.