Memory-Augmented LLM-based Multi-Agent System for Automated Feature Generation on Tabular Data
This work addresses the challenge of generating diverse, high-value features for machine learning on tabular data, which is crucial for accurate and generalizable models, though it appears incremental by building on recent LLM-based approaches.
The paper tackles the problem of automated feature generation for tabular data by proposing a memory-augmented LLM-based multi-agent system (MALMAS), which improves feature quality and diversity through iterative refinement, as demonstrated by extensive experiments on public datasets.
Automated feature generation extracts informative features from raw tabular data without manual intervention and is crucial for accurate, generalizable machine learning. Traditional methods rely on predefined operator libraries and cannot leverage task semantics, limiting their ability to produce diverse, high-value features for complex tasks. Recent Large Language Model (LLM)-based approaches introduce richer semantic signals, but still suffer from a restricted feature space due to fixed generation patterns and from the absence of feedback from the learning objective. To address these challenges, we propose a Memory-Augmented LLM-based Multi-Agent System (\textbf{MALMAS}) for automated feature generation. MALMAS decomposes the generation process into agents with distinct responsibilities, and a Router Agent activates an appropriate subset of agents per iteration, further broadening exploration of the feature space. We further integrate a memory module comprising procedural memory, feedback memory, and conceptual memory, enabling iterative refinement that adaptively guides subsequent feature generation and improves feature quality and diversity. Extensive experiments on multiple public datasets against state-of-the-art baselines demonstrate the effectiveness of our approach. The code is available at https://github.com/fxdong24/MALMAS