Preference-based abstract argumentation for case-based reasoning
Author(s)
Gould, Adam
Paulino Passos, Guilherme
Dadhania, Seema
Williams, Matt
Toni, Francesca
Type
Conference Paper
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.
Date Issued
2024-08-01
Date Acceptance
2024-07-11
Citation
Proceedings of the 21st International Conference on Principles of Knowledge Representation and Reasoning, 2024, pp.394-404
ISBN
978-1-956792-05-8
ISSN
2334-1033
Publisher
IJCAI Organization
Start Page
394
End Page
404
Journal / Book Title
Proceedings of the 21st International Conference on Principles of Knowledge Representation and Reasoning
Copyright Statement
© 2024 International Joint Conferences on Artificial Intelligence Organization.
Source
International Conference on Principles of Knowledge Representation and Reasoning
Subjects
Argumentation
Artificial Intelligence
Machine Learning
Publication Status
Published
Start Date
2024-11-02
Finish Date
2024-11-08
Coverage Spatial
Hanoi, Vietnam