Enhancing Prediction, Explainability, Inference and Robustness of Decision Trees via Symbolic Regression-Discovered Splits

Created by W.Langdon from gp-bibliography.bib Revision:1.7877

@InProceedings{fong:2024:GECCOcomp,

• author = "Kei Sen Fong and Mehul Motani",
• title = "Enhancing Prediction, Explainability, Inference and Robustness of Decision Trees via Symbolic {Regression-Discovered} Splits",
• booktitle = "Proceedings of the 2024 Genetic and Evolutionary Computation Conference Companion",
• year = "2024",
• editor = "Marcus Gallagher",
• pages = "37--38",
• address = "Melbourne, Australia",
• series = "GECCO '24",
• month = "14-18 " # jul,
• organisation = "SIGEVO",
• publisher = "Association for Computing Machinery",
• publisher_address = "New York, NY, USA",
• keywords = "genetic algorithms, genetic programming, symbolic regression, decision tree",
• isbn13 = "979-8-4007-0495-6",
• DOI = "doi:10.1145/3638530.3664067",
• size = "2 pages",
• abstract = "We introduce a hybrid machine learning algorithm that uses Genetic Programming-based Symbolic Regression (SR) to create decision trees (DT) with enhanced prediction, explainability, inference and robustness. Conventional DT algorithms for classification tasks are limited to axis-parallel splits. Thus, when the true boundaries do not align with feature axes, DT is likely to exhibit complex structures. In this work, we introduce SR-Enhanced DT (SREDT), which uses SR to increase the richness of the class of potential DT splits. We assess the performance of SREDT on both synthetic and real-world datasets. Despite its simplicity, our approach yields remarkably compact trees that surpass DT and its variant, oblique DT (ODT), in supervised classification tasks in terms of accuracy and F-score. SREDT possesses low depth, with a small number of leaves and terms, increasing explainability. SREDT also makes faster inference, even compared to DT. SREDT also demonstrates the highest robustness to noise. Furthermore, despite being a small white-box model, SREDT demonstrates competitive performance with large black-box tabular classification algorithms, including tree ensembles and deep models. This Hot-of-the-Press paper summarizes the work, K.S. Fong and M. Motani, {"}Symbolic Regression Enhanced Decision Trees for Classification Tasks{"}, The Annual AAAI Conference on Artificial Intelligence (AAAI'24).",
• notes = "GECCO-2024 A Recombination of the 33rd International Conference on Genetic Algorithms (ICGA) and the 29th Annual Genetic Programming Conference (GP)",

}

Genetic Programming entries for Kei Sen Fong Mehul Motani

Citations