Preference-Based Abstract Argumentation for Case-Based Reasoning (with Appendix)
This work addresses the need for more flexible and user-controllable interpretable models in domains like healthcare, though it appears incremental as it builds on existing abstract argumentation and case-based reasoning methods.
The paper tackles the problem of incorporating user-defined preferences into interpretable classification models by introducing Preference-Based Abstract Argumentation for Case-Based Reasoning (AA-CBR-P), which allows users to specify preferences over comparison approaches for cases. It demonstrates that this approach outperforms other interpretable models on a real-world medical dataset of brain tumor patients.
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.