Decision Tree Induction Through LLMs via Semantically-Aware Evolution

• URL: http://arxiv.org/abs/2503.14217v1
• Date: Tue, 18 Mar 2025 12:52:03 GMT
• Title: Decision Tree Induction Through LLMs via Semantically-Aware Evolution
• Authors: Tennison Liu, Nicolas Huynh, Mihaela van der Schaar,
• Abstract summary: We propose an evolutionary optimization method for decision tree induction based on genetic programming (GP)<n>Our key innovation is the integration of semantic priors and domain-specific knowledge about the search space into the algorithm.<n>This is operationalized through novel genetic operators that work with structured natural language prompts.
• Score: 53.0367886783772
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Decision trees are a crucial class of models offering robust predictive performance and inherent interpretability across various domains, including healthcare, finance, and logistics. However, current tree induction methods often face limitations such as suboptimal solutions from greedy methods or prohibitive computational costs and limited applicability of exact optimization approaches. To address these challenges, we propose an evolutionary optimization method for decision tree induction based on genetic programming (GP). Our key innovation is the integration of semantic priors and domain-specific knowledge about the search space into the optimization algorithm. To this end, we introduce $\texttt{LLEGO}$, a framework that incorporates semantic priors into genetic search operators through the use of Large Language Models (LLMs), thereby enhancing search efficiency and targeting regions of the search space that yield decision trees with superior generalization performance. This is operationalized through novel genetic operators that work with structured natural language prompts, effectively utilizing LLMs as conditional generative models and sources of semantic knowledge. Specifically, we introduce $\textit{fitness-guided}$ crossover to exploit high-performing regions, and $\textit{diversity-guided}$ mutation for efficient global exploration of the search space. These operators are controlled by corresponding hyperparameters that enable a more nuanced balance between exploration and exploitation across the search space. Empirically, we demonstrate across various benchmarks that $\texttt{LLEGO}$ evolves superior-performing trees compared to existing tree induction methods, and exhibits significantly more efficient search performance compared to conventional GP approaches.

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