Onishi, S.; Nishimura, M.; Fujimura, R.; Hayashi, Y. Why Do Tree Ensemble Approximators Not Outperform the Recursive-Rule eXtraction Algorithm? Mach. Learn. Knowl. Extr.2024, 6, 658-678.
Onishi, S.; Nishimura, M.; Fujimura, R.; Hayashi, Y. Why Do Tree Ensemble Approximators Not Outperform the Recursive-Rule eXtraction Algorithm? Mach. Learn. Knowl. Extr. 2024, 6, 658-678.
Onishi, S.; Nishimura, M.; Fujimura, R.; Hayashi, Y. Why Do Tree Ensemble Approximators Not Outperform the Recursive-Rule eXtraction Algorithm? Mach. Learn. Knowl. Extr.2024, 6, 658-678.
Onishi, S.; Nishimura, M.; Fujimura, R.; Hayashi, Y. Why Do Tree Ensemble Approximators Not Outperform the Recursive-Rule eXtraction Algorithm? Mach. Learn. Knowl. Extr. 2024, 6, 658-678.
Abstract
Machine learning models are increasingly being used in critical domains, but their complexity, lack of transparency, and poor interpretability remain problematic. Decision trees (DTs) and rule-based approaches are well-known examples of interpretable models, and numerous studies have investigated techniques for approximating tree ensembles using DTs or rule sets; however, tree ensemble approximators do not consider interpretability. These methods are known to generate three main types of rule sets: DT-based, unordered-based, and decision list-based. However, no known metric has been devised to distinguish and compare these rule sets. Therefore, the present study proposes an interpretability metric to allow comparisons of interpretability between different rule sets, such as decision list- and DT-based rule sets, and investigates the interpretability of the rules generated by the tree ensemble approximators. To provide new insights into the reasons why decision list-based and inspired classifiers do not work well for categorical datasets consisting of mainly nominal attributes, we compare objective metrics and rule sets generated by the tree ensemble approximators and the \textit{Recursive-Rule eXtraction algorithm (Re-RX) with J48graft}. The results indicated that \textit{Re-RX with J48graft} can handle categorical and numerical attributes separately, has simple rules, and achieves high interpretability, even when the number of rules is large.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
