Abstract
We marry two powerful ideas: decision tree ensemble for rule induction and abstract argumentation for aggregating inferences from diverse decision trees to produce better predictive performance and intrinsically interpretable than state-of-the-art ensemble models. Our approach called Arguing Tree Ensemble is a self-explainable model that first learns a group of decision trees from a given dataset. It then treats all decision trees as knowledgable agents and let them argue each other for concluding a prediction. Unlike conventional ensemble methods, this proposal offers full transparency to the prediction process. Therefore, AI users are able to interpret and diagnose the prediction’s output.
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Racharak, T. (2023). Interpretable Decision Tree Ensemble Learning with Abstract Argumentation for Binary Classification. In: Tanveer, M., Agarwal, S., Ozawa, S., Ekbal, A., Jatowt, A. (eds) Neural Information Processing. ICONIP 2022. Communications in Computer and Information Science, vol 1792. Springer, Singapore. https://doi.org/10.1007/978-981-99-1642-9_5
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