FeatureHospital: AI Framework Automates Multi-View Multi-Label Feature Selection

A new research paper on arXiv (2608.16148) introduces FeatureHospital, a skill-driven multi-agent framework designed to automate the customization of feature selection algorithms for multi-view multi-label data. The framework addresses the challenge that existing feature selection methods are often tailored to specific data characteristics, requiring significant expert knowledge and manual effort to adapt across diverse datasets. FeatureHospital operates by first diagnosing the target dataset to identify its feature selection issues, then deploying specialist agents equipped with domain-specific skills to design a suitable algorithm. This approach aims to reduce the time and labor costs associated with traditional algorithm design, potentially accelerating the adoption of feature selection in practical applications. The paper is authored by researchers and was announced as a new submission on arXiv. The framework represents a step toward automated machine learning (AutoML) in the niche area of multi-view multi-label feature selection, which is relevant to fields such as bioinformatics, multimedia analysis, and text mining where data often come from multiple views and have multiple labels.