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[2602.17641] FAMOSE: A ReAct Approach to Automated Feature Discovery
Summary
The paper presents FAMOSE, a novel framework that utilizes the ReAct paradigm for automated feature discovery in machine learning, enhancing feature generation and selection for regression and classification tasks.
Why It Matters
FAMOSE addresses a critical challenge in machine learning—feature engineering—by automating the process, which can save time and improve model performance. This innovation is particularly relevant as the demand for efficient machine learning solutions grows, making it a significant contribution to the field.
Key Takeaways
- FAMOSE automates feature discovery, reducing reliance on domain expertise.
- It integrates feature selection and evaluation within an agent architecture.
- Demonstrates state-of-the-art performance in both classification and regression tasks.
- Utilizes the ReAct framework to enhance iterative feature evaluation.
- Highlights the effectiveness of AI agents in solving complex feature engineering problems.
Computer Science > Machine Learning arXiv:2602.17641 (cs) [Submitted on 19 Feb 2026] Title:FAMOSE: A ReAct Approach to Automated Feature Discovery Authors:Keith Burghardt, Jienan Liu, Sadman Sakib, Yuning Hao, Bo Li View a PDF of the paper titled FAMOSE: A ReAct Approach to Automated Feature Discovery, by Keith Burghardt and 4 other authors View PDF HTML (experimental) Abstract:Feature engineering remains a critical yet challenging bottleneck in machine learning, particularly for tabular data, as identifying optimal features from an exponentially large feature space traditionally demands substantial domain expertise. To address this challenge, we introduce FAMOSE (Feature AugMentation and Optimal Selection agEnt), a novel framework that leverages the ReAct paradigm to autonomously explore, generate, and refine features while integrating feature selection and evaluation tools within an agent architecture. To our knowledge, FAMOSE represents the first application of an agentic ReAct framework to automated feature engineering, especially for both regression and classification tasks. Extensive experiments demonstrate that FAMOSE is at or near the state-of-the-art on classification tasks (especially tasks with more than 10K instances, where ROC-AUC increases 0.23% on average), and achieves the state-of-the-art for regression tasks by reducing RMSE by 2.0% on average, while remaining more robust to errors than other algorithms. We hypothesize that FAMOSE's strong performance is ...
