FeatureHospital: A Skill-Driven Multi-Agent Framework for Automated Algorithm Customization in Multi-View Multi-Label Feature Selection

2026-08-17

AI Summary

This study addresses the limitations of existing multi-view multi-label feature selection algorithms, which typically rely on expert knowledge and struggle with heterogeneous data. To overcome these challenges, this work proposes a novel skill-driven multi-agent framework that enables customized algorithm generation and collaborative optimization through automatic dataset diagnosis, domain skill library invocation, and dynamic objective function construction. Breaking from traditional manual design paradigms, the proposed approach adaptively generates efficient feature selection algorithms tailored to specific data characteristics. This method significantly reduces design costs while enhancing algorithmic performance, thereby establishing a new paradigm for automated machine learning in complex scenarios.

Abstract

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.

Problem

• Multi-view multi-label feature selection
• Automated algorithm design
• Heterogeneous data
• Expert knowledge dependency

Innovation

• Skill-driven Multi-Agent Framework
• Automated Algorithm Customization
• Multi-View Multi-Label Feature Selection
• Dataset Diagnosis
• Objective Function Integration