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When a CBR in Hand is Better than Twins in the Bush
Conference paper   Open access

When a CBR in Hand is Better than Twins in the Bush

Mobyen Uddin Ahmed, Shaibal Barua, Shahina Begum, Mir Riyanul Islam and Rosina O Weber
CEUR workshop proceedings, pp 141-152
Sep 2022
DOI: https://doi.org/10.48550/arxiv.2305.05111
url
https://ceur-ws.org/Vol-3389/ICCBR_2022_Workshop_paper_99.pdfView
Published, Version of Record (VoR) Open CC BY V4.0

Abstract

Part of the XCBR: Workshop on Case-Based Reasoning for the Explanation of Intelligent Systems AI methods referred to as interpretable are often discredited as inaccurate by supporters of the existence of a trade-off between interpretability and accuracy. In many problem contexts however this trade-off does not hold. This paper discusses a regression problem context to predict flight take-off delays where the most accurate data regression model was trained via the XGBoost implementation of gradient boosted decision trees. While building an XGB-CBR Twin and converting the XGBoost feature importance into global weights in the CBR model, the resultant CBR model alone provides the most accurate local prediction, maintains the global importance to provide a global explanation of the model, and offers the most interpretable representation for local explanations. This resultant CBR model becomes a benchmark of accuracy and interpretability for this problem context, and hence it is used to evaluate the two additive feature attribute methods SHAP and LIME to explain the XGBoost regression model. The results with respect to local accuracy and feature attribution lead to potentially valuable future work.

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