SPIO: Ensemble and Selective Strategies via LLM-Based Multi-Agent Planning in Automated Data Science

• URL: http://arxiv.org/abs/2503.23314v1
• Date: Sun, 30 Mar 2025 04:45:32 GMT
• Title: SPIO: Ensemble and Selective Strategies via LLM-Based Multi-Agent Planning in Automated Data Science
• Authors: Wonduk Seo, Juhyeon Lee, Yi Bu,
• Abstract summary: Large Language Models (LLMs) have revolutionized automated data analytics and machine learning by enabling dynamic reasoning and adaptability.<n>We propose SPIO, a novel framework that orchestrates multi-agent planning across four key modules.<n>In each module, dedicated planning agents independently generate candidate strategies that cascade into subsequent stages, fostering comprehensive exploration.
• Score: 1.1343849658875087
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Large Language Models (LLMs) have revolutionized automated data analytics and machine learning by enabling dynamic reasoning and adaptability. While recent approaches have advanced multi-stage pipelines through multi-agent systems, they typically rely on rigid, single-path workflows that limit the exploration and integration of diverse strategies, often resulting in suboptimal predictions. To address these challenges, we propose SPIO (Sequential Plan Integration and Optimization), a novel framework that leverages LLM-driven decision-making to orchestrate multi-agent planning across four key modules: data preprocessing, feature engineering, modeling, and hyperparameter tuning. In each module, dedicated planning agents independently generate candidate strategies that cascade into subsequent stages, fostering comprehensive exploration. A plan optimization agent refines these strategies by suggesting several optimized plans. We further introduce two variants: SPIO-S, which selects a single best solution path as determined by the LLM, and SPIO-E, which selects the top k candidate plans and ensembles them to maximize predictive performance. Extensive experiments on Kaggle and OpenML datasets demonstrate that SPIO significantly outperforms state-of-the-art methods, providing a robust and scalable solution for automated data science task.

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