FeatureHospital：一种用于多视图多标签特征选择中自动化算法定制的技能驱动多智能体框架
FeatureHospital: A Skill-Driven Multi-Agent Framework for Automated Algorithm Customization in Multi-View Multi-Label Feature Selection
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中文总结 AI 辅助
FeatureHospital是技能驱动的多智能体框架，可自动化设计多视图多标签特征选择算法，经实验验证能为不同数据集构建有效算法。
中文摘要 AI 辅助
多视图多标签特征选择旨在从异构视图中识别出紧凑且信息丰富的特征子集，同时保留多个标签的判别信息。现有方法通常从特定建模视角开发，并结合针对特定数据特征定制的机制。针对具有多样异构特征的数据集设计合适的特征选择算法，仍在很大程度上依赖专家知识和大量人工工作，带来了可观的时间和人力成本，严重阻碍了特征选择的实际应用。为解决该问题，我们提出FeatureHospital，一种用于自动化多视图多标签特征选择算法设计的技能驱动多智能体框架。FeatureHospital首先对目标数据集进行诊断，以识别其特征选择问题；基于诊断结果，配备领域技能的专家智能体针对不同问题开出相应的优化策略和损失项；之后协调生成的方案以消除重叠并解决冲突，再将其整合为紧凑的特定数据集目标；最后优化构建的目标以选择最终的特征子集。实验结果表明，FeatureHospital能够根据不同数据集的各自特征为其构建有效的特征选择算法。
英文摘要
Multi-view multi-label feature selection aims to identify a compact and informative feature subset from heterogeneous views while preserving discriminative information for multiple labels. Existing methods are generally developed from specific modeling perspectives and incorporate mechanisms tailored to particular data characteristics. Designing suitable feature selection algorithms across datasets with diverse and heterogeneous characteristics still relies heavily on expert knowledge and substantial manual effort, imposing considerable time and labor costs that severely hinder the practical adoption of feature selection. To address this problem, we propose FeatureHospital, a Skill-driven multi-agent framework for automated multi-view multi-label feature selection algorithm design. FeatureHospital first diagnoses the target dataset to identify its feature selection issues. Based on the diagnosis, specialist agents equipped with domain Skills then prescribe corresponding optimization strategies and Loss terms for different issues. After that, the resulting prescriptions are reconciled to remove overlaps and resolve conflicts before being integrated into a compact dataset-specific objective. Finally, the constructed objective is optimized to select the final feature subset. Experimental results demonstrate that FeatureHospital can construct effective feature selection algorithms for different datasets based on their individual characteristics.
