Collaborative Multi-Agent Reinforcement Learning for Automated Feature Transformation with Graph-Driven Path Optimization
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arXiv
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| Autores principales: | , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866912344082219008 |
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| author | Huang, Xiaohan Wang, Dongjie Ning, Zhiyuan Qiao, Ziyue Long, Qingqing Zhu, Haowei Du, Yi Wu, Min Zhou, Yuanchun Xiao, Meng |
| author_facet | Huang, Xiaohan Wang, Dongjie Ning, Zhiyuan Qiao, Ziyue Long, Qingqing Zhu, Haowei Du, Yi Wu, Min Zhou, Yuanchun Xiao, Meng |
| contents | Feature transformation methods aim to find an optimal mathematical feature-feature crossing process that generates high-value features and improves the performance of downstream machine learning tasks. Existing frameworks, though designed to mitigate manual costs, often treat feature transformations as isolated operations, ignoring dynamic dependencies between transformation steps. To address the limitations, we propose TCTO, a collaborative multi-agent reinforcement learning framework that automates feature engineering through graph-driven path optimization. The framework's core innovation lies in an evolving interaction graph that models features as nodes and transformations as edges. Through graph pruning and backtracking, it dynamically eliminates low-impact edges, reduces redundant operations, and enhances exploration stability. This graph also provides full traceability to empower TCTO to reuse high-utility subgraphs from historical transformations. To demonstrate the efficacy and adaptability of our approach, we conduct comprehensive experiments and case studies, which show superior performance across a range of datasets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_17355 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Collaborative Multi-Agent Reinforcement Learning for Automated Feature Transformation with Graph-Driven Path Optimization Huang, Xiaohan Wang, Dongjie Ning, Zhiyuan Qiao, Ziyue Long, Qingqing Zhu, Haowei Du, Yi Wu, Min Zhou, Yuanchun Xiao, Meng Machine Learning Artificial Intelligence Feature transformation methods aim to find an optimal mathematical feature-feature crossing process that generates high-value features and improves the performance of downstream machine learning tasks. Existing frameworks, though designed to mitigate manual costs, often treat feature transformations as isolated operations, ignoring dynamic dependencies between transformation steps. To address the limitations, we propose TCTO, a collaborative multi-agent reinforcement learning framework that automates feature engineering through graph-driven path optimization. The framework's core innovation lies in an evolving interaction graph that models features as nodes and transformations as edges. Through graph pruning and backtracking, it dynamically eliminates low-impact edges, reduces redundant operations, and enhances exploration stability. This graph also provides full traceability to empower TCTO to reuse high-utility subgraphs from historical transformations. To demonstrate the efficacy and adaptability of our approach, we conduct comprehensive experiments and case studies, which show superior performance across a range of datasets. |
| title | Collaborative Multi-Agent Reinforcement Learning for Automated Feature Transformation with Graph-Driven Path Optimization |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2504.17355 |